Patent Grant
7622779
The present invention relates generally to the field of chip design and fabrication. The present invention also relates generally to the field of circuit routing.
With submicron technology, large numbers of processors, elements, or devices can be integrated on microcircuit chips. The processors, elements, or devices are arranged in arrays of cells on one or more layers of a chip. Each of the cells, containing a component of one or more overall circuits, contains one or more terminals for communicating with other cells. To permit the cells to communicate with one another interconnects, such as routing wires or other conductive paths, connect the cells and/or bus segments, which themselves interconnect groups of cells.
The interconnects are arranged in meshes formed in or among one or more interconnect layers (also known as routing layers) of a microcircuit chip. A mesh is a common routing architecture for many reconfigurable computing systems. Both conventional and more recently proposed on-chip multiprocessor systems use mesh networks as communication backbones.
The microcircuit chips typically include a plurality of interconnect layers for interconnection of the cells. Pluralities of layers are often used for individual interconnections due to design constraints, for example. Vias help to route the interconnects between pluralities of layers. Connections are switched by devices such as, but not limited to, metal oxide semiconductor (MOS) devices.
High-performance system-on-a-chip (SoC) requires nonblocking interconnects between the array of cells on the chip. With nonblocking interconnects, when a cell needs to communicate with another cell, a route always exists for communication.
Interconnects have become one of the most precious resources on a chip. Length of connection between cells is a limiting performance factor in terms of power consumption and latency, among other factors. Unreasonable distribution of interconnect resources results in bottlenecks that stall data flow, while leaving other routing resources wasted. Furthermore, it is impractical to resolve this problem merely by enlarging a channel capacity of an entire array.
A long path through interconnects increases power consumption and signal delay. Additionally, a common physical embodiment of multiprocessor arrays is CMOS technology. In CMOS technology, power dissipation is proportional to interconnect capacitance, which in turn is proportional to a distance traveled by a signal. Thus, it is highly desirable to provide an architecture in which interconnection length is minimized. It is also desirable to provide an architecture that includes the shortest totals of route lengths between processors, and not interconnect length alone.
One predominant type of interconnect mesh is Manhattan architecture, so-called because its rectilinear connection arrangement resembles a city street grid. Manhattan architecture, however, requires lengths of interconnects that far exceed actual (Euclidean) distances between individual cells due to, for example, the requirement for orthogonal circuit paths.
More recently, an alternative chip architecture known as X-architecture has been introduced to reduce interconnection lengths versus Manhattan architecture. X-architecture uses tree structures having recursive patterns to interconnect cells in a nonblocking interconnection architecture. The tree structures may take the form of H-shaped patterns or X-shaped patterns, with the cells located at the extremities of each pattern. The interconnects are oriented, for example, in 0°, 45°, 90°, and 135° directions. X-architecture has been disclosed as a solution to address microcircuit chip designs, especially chips with five or more routing layers.
Interconnection between all cells is provided by a specific hierarchical structure. For example, at a level “zero”, four cells may be interconnected by an “X”. At a higher level, say, level “1”, four level “zero” “X's” are interconnected by a larger “X”. At a still-higher level (“2”), four level “1” “X's” are interconnected by a still-larger “X”, etc. Performance improvement of the X-architecture over the Manhattan architecture has been demonstrated.
The present invention provides, among other features, a multi-celled chip. The chip includes arrays of hexagonal cells arranged on at least one component layer. A plurality of interconnects including Y's that connect the cells in clusters of three cells each. Each of the Y's has a node and three interconnects connecting the node to respective ones of the cells within a cluster, wherein each Y connects each cell of its respective cell group to the node.
The present invention also provides a number of methods to assess particular interconnection architectures, including providing a cost function and an assessment method based on a multi-commodity flow model. Exemplary embodiments of chips and interconnection architectures are also provided that are selected using the assessment methods provided. Bridges are also provided for directly connecting cells of a chip, and methods are provided for determining optimum locations of the bridges.
Interconnections among the cell array reveal themselves as a key problem, as the interconnect becomes one of the most precious resources on a chip. With the advent of deep sub-micron technologies, switches are becoming less costly, yet interconnects such as wires are still expensive. Therefore, optimization efforts according to embodiments of the present invention focus on the interconnect resources.
Traditional Manhattan interconnect architecture organizes interconnects on two orthogonal routing directions, 0° and 90°, for the simplicity of routing embedding and design rule checking. However, this artificial restriction on routing directions adds significant interconnect length compared with the Euclidean optimum, and thus decreases the communication capability of the on-chip interconnects.
One goal of certain embodiments of the present invention is to allocate channel capacities in a mesh routing architecture to improve, or maximize, its communication capability. Communication capability can be measured by the throughput, which is the amount of information that every pair of nodes can exchange simultaneously. Throughput is a function of channel capacity and the dimension of the processor array.
Chips have been disclosed including non-rectilinear interconnects to improve the efficiency of on-chip interconnects. Most of these chips have introduced 45° short jogs to improve routability of the chip in the detailed routing stage. Even in this architecture, however, the majority of the interconnects on the chip have still been routed in directions of either 0° or 90°.
As an alternative to the traditional, Manhattan architecture, Mutrunoi et al. proposed an on-chip architecture known as X-architecture, which is designed to target designs having five or more routing layers. I. Mutsunori, T. Mitsuhashi, A. Le, S. Kazi, Y. Lin, A. Fujimiura, and S. Teig, “A Diagonal Interconnect Architecture and Its Application to RISC Core Design,” Proc. ISSCC, pp. 684-689, San Jose, Calif., February 2002. In X-architecture, interconnects are arranged in 0°, 45°, 90°, and 135° directions. This design has been shown to achieve significant chip performance improvement and power reduction over Manhattan architecture.
However, with X-architecture, it is possible for two nodes to be physically adjacent on a chip layer and yet be on different tree structures on the same level. Furthermore, these respective tree structures may be linked to separate tree structures on a higher level, or even a still-higher level, until a level is reached, called a root, that is a common ancestor to the cells. Consequently, a greatly extended path length through interconnects may have to be traversed to interconnect two cells even through they may be physically adjacent. It is desirable to gain still further improvement in performance, including power consumption and speed.
Another constraint on throughput of an active device array is the problem of getting a signal or power from one area, for example a quadrant, of a chip to another. To do so, a middle row or middle column in the interconnect mesh typically must be traversed. Due to the normal distribution of interconnections, a middle row or middle column of the interconnect mesh tends to create a bottleneck effect. Enlarging the congested area will not itself produce better throughput. It is therefore desirable to provide an improved geometry to increase throughput.
According to an embodiment of the present invention, a configuration is provided in which an interconnect architecture includes one or more Y's to connect clusters of cells. A Y is a structural routing model in which interconnects, or legs, extend in three separate directions from a common node. An architecture formed of Y's is termed herein a Y-tree, and allows interconnection among some or all cells in a hexagonal pattern. Groups of Y's routed together form Y-trees. In an exemplary embodiment, individual Y's on a particular level connect clusters of cells, and these Y's are interconnected by Y's on higher levels. In the higher levels, a Y on a next-higher level is preferably rotated with respect to the Y on the next-lower level.
For example, an interconnect mesh having Y-architecture is provided in a multi-element integrated circuit chip array. Interconnects are routed in three directions, e.g. 0°, 60°, and 120°; or 0°, 120°, and 240°. The mesh preferably comprises a plurality of layers. In an additionally preferred aspect, the cells are arranged in a hexagonal array and embodied in a chip having a shape of a convex polygon, such as a hexagonal chip. Individual Y's connect clusters of the hexagonal cells. Diagonal routing technology allows different arrangements of interconnect structure. Methods for fabricating diagonal routing are provided in, for example, I. Mutsunori, T. Mitsuhashi, A. Le, S. Kazi, Y. Lin, A. Fujimiura, and S. Teig, “A Diagonal Interconnect Architecture and Its Application to RISC Core Design,” Proc. ISSCC, pp. 684-689, San Jose, Calif., February 2002.
In a preferred embodiment, the hexagonal cell array produces a flow congestion pattern that does not include the center of the hexagonal pattern. However, the benefit of producing a flow congestion pattern that does not include the center of the hexagonal pattern is not a function of any particular values of angles between the legs of individual Y's. Particular angles of the legs are not required; for example, 0°, 60°, and 120°, are merely an exemplary choice for artwork design. However, legs in one cell should be configured to connect with legs in a next cell. Wide tolerances between the specific values of the tree angles are allowed, while providing the same utility of the Y's. For example, a Y having legs at 0°, 150°, and 210° (forming a more traditional “Y” shape) could be provided.
The hexagonal cell array also has the property of hierarchical expansion. An algorithm is provided to set up a hierarchical tree of interconnect, and another algorithm is provided to set up a communication route in the architecture for pairs of processors in the array. It has been determined that the Y-architecture approaches the X-architecture in terms of optimizing wire resources. Additionally, algorithms for the merge of polygons on a hexagonal backbone are provided, which is useful in analysis of very large Y-trees.
According to an additional embodiment of the present invention, a cost function is provided to balance the cost of interconnect resources and the power consumption for the interconnect topology on a cell array. The total interconnect length is used to measure the cost of the interconnect resources, and the length of signal paths is used to evaluate the power consumption, since the power consumption is proportional to the interconnect capacitance, which in turn is proportional to the traveling distance of a signal.
An exemplary application of the cost function is used herein to compare shapes of meshes of cells. Each form of connection can be arranged in differently shaped meshes. For example, Manhattan architecture is most readily arranged in a square mesh; however, it may also be embodied in a diamond-shaped mesh, which may be visualized as a square rotated by 45° from the position in which it rests on a side. Furthermore, the X-architecture lends itself to arrangement in an octagonal mesh, among other mesh shapes. To provide geometry less susceptible to bottlenecks, embodiments of the present invention provide alternative polygonal meshes, which may be formed using dies, for example.
According to an exemplary application of the cost function, the X-type nonblocking architecture (“X-architecture”) has been found to have a good tradeoff for a two-dimensional processor array. A significant benefit to X-architecture is that it can be hierarchically expanded. This benefit has been shown to be applicable to Y-architecture as well. The X-architecture and Y-architecture, along with other architectures, can be compared using the provided cost function.
Methods also are provided for determining locations of optimal additional interconnects between certain cells, buses, and/or switches. These methods help to overcome some of the deficiencies in prior architectures, while continuing to require a minimum cost of interconnects and communication resources.
A method for assessing routing architecture is also provided. The Y-architecture of the present invention, having three routing directions, is compared with the Manhattan architecture and the X-architecture (with two and four routing directions, respectively). Using Y-architecture potentially gains a throughput improvement of 33.3% over the traditional Manhattan architecture on a square mesh. The Y-architecture produces nearly the same (2.6%) throughput as the X-architecture on a square mesh, yet using one less routing direction.
Furthermore, the Y-architecture achieves an average of 13.4% interconnect length reduction over Manhattan architecture, and approaches (4.3% less) the reduction of the X-architecture, while providing a simpler design. Still further, making the shape of the chip a convex polygon, and preferably closer to a circle, significantly improves the throughput over the rectangular chip. Using Y-architecture, a hexagonal chip can produce 41% more throughput than a square chip using Manhattan architecture, without causing dead space on the wafer.
The described Y-architecture and other optimization methods are applicable not only to chip design, but to other areas such as, but not limited to, wireless communications. In an exemplary wireless communication design base stations may be seated at the centers of the hexagonal areas in an array, and a route between the base stations may form the main part of the wireless communication route. The high performance solutions to communication among the base stations are quite similar to those of an array of processors on a chip. Therefore, the methods described herein are applicable to optimization of interconnect of base stations to balance cable resources and power consumptions.
Referring now to the drawings,
As shown in
As also shown in
In the Y-tree 120 shown in
The Y-architecture preferably is routed upon the hexagonal array 102 including a number of rows. This array 102 preferably has the following properties: (1) a ½ grid shift exists between rows; (2) each cell 104 is physically adjacent to two cells in the same row; and (3) each cell is physically adjacent to two cells in the neighboring row above and two cells in the row below. Depending on the orientation, these rules can be respectively applied instead to columns. Thus, groups of three neighboring cells 102 can be clustered to set up individual Y's 108. Exemplary clusters 106 of three cells 104 are shown in bold in the hexagonal array of
If each cluster 106 of three cells 104 is regarded as a unit, it can be seen that the hexagonal array 102 composed of such units also has the property of ½ grid shift, but now in the vertical direction (in the orientation shown). Thus, the Y-architecture can be expanded to the second level 116; however, the directions of the individual Y's 108 at the second level 116 have a rotation of 90° compared to the Y's of the first level 110. In a preferred embodiment, this property of ½ grid shift always holds when the Y-architecture is continuously expanded to upper levels. As shown in
For a Y-architecture of n tree levels, there are 2n combinations of orientations of the n Y's 108 on different tree levels. A combination of Y's 108 is referred to herein as a configuration, which indicates the way the overall Y-architecture grows. The configuration results in a particular boundary for the cells 104 interconnected by the Y-architecture.
Given a particular configuration, C, a Y-architecture as shown in
According to the exemplary algorithm Setup_Y_tree shown in
From the hexagonal array's 102 properties and the algorithm for setting up the Y-trees 120, it can be shown that: (1) the exemplary algorithm according to an embodiment of the present invention generates Y-tree architecture without cell overlapping; (2) the number of cells covered by the generated Y-tree of n levels is 3n; and (3) the length of the trunk at level n is (1/√{square root over (3)})n.
In another embodiment of the present invention, a merging algorithm is used to merge two polygons. Then, based on this algorithm, another algorithm for merging polygons to set up a Y-tree without empty cells is provided.
Suppose there exists a polygon 122 on a backbone of hexagons, as shown in
A sequence, termed herein a hexagonal sequence, can thus be determined to represent the polygon 122. Starting with the edge 124 of the boundary of the polygon and traveling counterclockwise, if the edge 124 has a positive rotation, a 1 is entered into the sequence. If the edge 124 has a negative rotation, a 0 is entered into the sequence. The resulting string represents the hexagonal sequence. If A denotes a hexagonal sequence, then A(i) is defined to refer to the ith element in A, where A(i)ε0,1.
For example, the hexagonal sequence of the polygon 122 shown in
It can be seen that one can make any bits barrel-shift (assumed herein, for consistency only, to be leftwards) on a non-oriented hexagonal sequence without changing the corresponding polygon 122. Furthermore, for a correct hexagonal sequence, the number of 1's will be six more than the number of 0's, while for any sub-sequence, the difference between the number of 1's and the number of 0's should not exceed five. It can also be seen that two polygons 122 have the same shape and area (assuming unit size of cells) if they have the same hex-sequence. Additionally, if polygon 122 is flipped, its hex-sequence is also horizontally flipped. Thus, for a symmetric polygon, the hex-sequence should be unchanged if the polygon 122 is flipped.
In an exemplary merging method, if rotations are not permitted for generation of Y-trees 120, a definition is assumed for an oriented hex-sequence. Every edge 124 on the polygon 122 thus has only three possible directions, and an oriented hexagonal sequence is denoted by starting the hexagonal sequence with a vertical edge that is traversed downwardly. Therefore, the oriented hex-sequence for the polygon shown in
The direction of each edge 124 can be calculated easily according to the numbers of “1's” and “0's” ahead of the edge. For an oriented hexagonal sequence A, i bits can be made to barrel-shift on the oriented hex-sequence without changing the direction of the polygon 122 if, and only if, the difference between the number of “1's”, and the number of “0”s is either zero or five in the subsequence from A(2) to A(i+1).
Given two hex-sequences A and B, an algorithm may be used to provide a new hex-sequence C, which is a merging of polygons A and B. To merge the hex-sequences, it is first assumed that both polygons A and B can be rotated. A preferred embodiment of the algorithm is shown in
Next, the situation is considered in which the polygons 122 are not allowed to rotate. In other words, two oriented polygons 122 are merged. The design of the function “Accept” in the algorithm of
In implementing this algorithm, A=1100111011101011; and B=101110111011 (remembering the position of the first bit in B). Thus:
The final polygon 122 of a complete Y-tree 120 can be obtained by merging the polygons of sub-trees, from the lowest level to the highest. When two polygons 122 are merged, they have a section of common boundary. The two ends of the common boundary may be connected with a line, in which the direction of the line is defined to be the direction of the common boundary.
Given the direction of the first edge 124 of the common boundary and the corresponding sub-sequence, the direction of the common boundary can be easily calculated. Merging of three polygons 122 of sub-trees can be realized, under the orientation configuration of Y at each level, by two steps:
For simplicity, one starts from the sub-tree of level 2. A subtree of level 2 includes 9 basic hexagonal cells 104 and has a completely symmetrical polygon regardless of the directions of the Y's on the first and second levels.
In multilayer routing, a via is used to connect interconnects that are disposed on multiple layers. However, the via blocks wire tracks on layers it passes through. According to another embodiment of the present invention, tunnel detours are used to route interconnects around vias.
To maximize throughput, a plurality of tunnels 134 preferably forms a bank 138, which is arranged on a lower routing layer along a direction of a plurality of gaps and vias 132. As shown in
The pattern formed by the five interconnects shown in
The design of early blocking networks focused on minimization of switches. In deep submicron technology, devices are shrunk to very small sizes and are less expensive, while interconnects such as wires and buses are lengthened, resulting in the increase of interconnect resistance and capacitance. Performance such as power consumption and signal delay are significantly deteriorated. Therefore, the length of signal paths is more important than the number of switches in the path regarding delay in circuit processing. However, a large-scale system on a chip (SoC) requires a significant amount of wire resources, so it is not feasible to set up the shortest path for every pair of processors in the array.
Conventionally, bus-based architectures have offered standards for communication interfaces. However, in chip design, a length of connection between cells is a limiting performance factor in terms of power consumption and latency, among other factors. The physical size of long interconnects limits the scalability of the architecture. Also, the contention for the interconnects adds to the latency of the communication. This increase is made more significant by the ever-shrinking size of individual cells and interconnects (in width, for example). Thus, chip designs minimizing connection lengths provide a performance benefit for a particular chip.
The total length may be used to measure the cost of interconnect resources, because the interconnect length typically is proportional to the amount of area taken on the routing layers. In deep submicron technologies, the number of routing layers remains limited. Furthermore, even as the number of routing layers increases, the coupling capacitance due to congestion and the required vias that connect signals to the layers high above make routing area a precious resource. It is also desirable to reduce the power dissipation of the wire interconnects because power consumption has become one of the main concerns in various applications.
According to a preferred method of the present invention, an objective cost function is provided to balance interconnect topology between routing area and power dissipation. This cost function is defined with an emphasis on interconnects as opposed to switches.
A goal of the cost function is to reduce the total traveled distance of the signal communication. Let us assume that each cell has to communicate with the rest of the cells with equal demand. Then, the total power dissipation is measured by the total pairwise distance between the cells. This equal demand model is used for preferred embodiments of the present method because the demand is symmetrical and thus independent of the placement implementation.
It is conceivable that by adding interconnects for the communication, the traveling distance can be reduced. However, the interconnects resources are limited by the physical space. Furthermore, the same resources are needed for other purposes such as, but not limited to, making internal connections within each cell, or for testing. Thus, the product of the total interconnect length and the total power distribution is chosen as a metric to balance design. Moreover, the derivative of this product provides an additional metric to further analyze the interconnect architecture.
A preferred method for determining benefit of a particular tree structure thus includes minimization of a cost function, as shown below.
Min M=L*D
In this definition, di,j is the shortest route length between processor (i) and processor (j). P is the total number of processors.
In conventional hierarchical interconnection architectures, either L or D of the cost function above has been minimized at the expense of increasing the value of the other parameter. The Y-structure of the present invention, on the other hand, helps to minimize or substantially reduce M. In a preferred integrated circuit design method of the present invention, the cost function is utilized for various configurations, and a configuration that minimizes M may thus be selected for design of an integrated circuit.
For rectangular cells, X-architecture provides optimal performance according to the above cost function.
The interconnects from the four cells 140 are also bundled together, forming a new interconnect going to a higher level, as shown in
Assuming the distance between the cells 140 is equal to one, the table of
Assuming that the distance between the centers of adjacent cells 140 is equal to α, the key results of the cost function as applied to the Y-architecture are shown in the table of
In an exemplary method comparing Y-architecture to X-architecture, it is assumed that the cells 140 in the two architectures have one unit area. Thus, the distance between the centers of adjacent cells 140 in X-architecture is one, and the distance between the centers of adjacent cells in the Y-architecture, αin the table of
To make a comparison for greater n levels, we neglect the lower order items of MX and MY:
To compare the respective performance of the trees mathematically, we assume that the trees 120, 148 cover the same number of cells 140. This results in:
A is the number of cells 140, 104 in the X- or Y-tree. As shown, there is just a slight difference between MX and MY. Therefore, the two architectures have similar performance. If A is close to some order of four, X-architecture is a preferred solution, while if A is close to some order of three, Y-architecture is preferred.
The derivative form of the cost function may also be used to further analyze the interconnect architecture, and is given by:
The last equation is based on the assumption that L/ΔL is much larger than one. To identify the most cost-effective incremental improvement due to the change of L, a derivative benefit is provided. The derivative benefit I is:
A negative sign is used because D is expected to decrease when L increases.
Based on examples of the cost function, one-dimensional, two-dimensional, and three-dimensional nonblocking interconnect architectures can be compared, and preferred structures can be selected. An embodiment of the present invention provides, among other things, a hierarchical interconnection architecture in which bridges are provided between physically proximate nodes that may otherwise be distant via interconnect routing. The bridge is preferably placed between nodes on the same level. A method is provided to select an optimum level on which to provide a bridge. Making a bridge between nodes perturbs the tree structure, and an optimal solution is derived in terms of derivative benefit.
Applying the above cost function to the architectures of
Similarly, the cost function can be applied to two-dimensional architectures.
Though the model shown in
Models of
The model of
According to certain embodiments of the present method, the cost function described above can be applied to improve particular interconnection architectures. For example,
A principal shortcoming of this structure is the extra detouring problem. An extreme example of this is depicted in
To reduce this shortcoming, and according to an embodiment of the present invention, interconnections referred to herein as bridges 170 are added to connect (bridge) nodes of the same level. As shown in
A preferred method of choosing optimum locations of the bridges 170 is provided. Given an n-level tree structure, for each integer m (0≦m<n), the incremental improvement of level-m nodes is stated as follows.
(1) Two level-m nodes (the T joints of the H tree) are considered physically adjacent if the Euclidean distance between the pair is the closest among all level-m nodes.
(2) A pair of level-m nodes is connected if the nodes in the pair are physically adjacent and if their lowest common ancestor of the tree structure is the root. Level-m nodes are linked with 2m buses.
A question is then presented as to the level for establishing the bridges 170 to obtain the largest benefit. To resolve this, a derivative benefit function is derived according to the derivative benefit defined above. Given a tree of level n, and the level investigated m:
ΔD(n,m)=A(n,m)*B(n,m)
In this equation, A (n, m) represents the number of pairs of cells 140 that will benefit from the addition of the bridges 170, and B (n, m) represents the route length saved due to the bridges. Thus, if m is odd:
For example, in the architecture of
From the above inequality, it can be shown that I (n, m)<I (n, m−1). Hence, in this example, only the odd levels are inspected for maximum derivative benefit. For a continuous variable function:
we calculate that when x=n−1, the derivative benefit is maximized, I(n,n−3)=2n−1.
Thus, for an H-tree architecture, level m=n−3 gives an optimal derivative benefit for bridges 170: Imax=2n−1.
In another example, the bridges 170 are added to the X-tree architecture model according to
The bridges 170 are added to the architecture of
Given an n-level X-tree structure, and using the method described above, incremental improvements are considered by using the bridges 170 to link nodes 114 at different levels. For each level m: 0≦m<n, pairs of level-m nodes 114 are connected if the pairs are physically adjacent and their lowest common ancestor in the X-tree is the root. Level-m nodes 114 are linked with 4m interconnects 130. The derivative benefit is derived as follows:
For the continuous variable function: I(n,x)=√{square root over (2)}*2n+x−22x*(√{square root over (2)}+1), there exists an x0=1<n−x0<2, such that I (n, x0) has a maximum value. Further calculation shows that I (n, n−2)>I (n, n−1). Therefore, it is preferred that, for an X-tree architecture, level m=n−2 gives the best derivative benefit for additional interconnects: 22(n−2)[25/2−(√{square root over (2)}+1)].
Another example of providing the bridges 170 is given with respect to Y-architecture.
However, the rotation of Y's 108 presents additional difficulty for adding bridges 170. The interconnection architecture that is shown in
The optimization method described above can be used to determine the derivative benefit for a Y-architecture with dead cells 178.
The optimal level on which to put the bridges 170 is level n−2, with the maximum incremental benefit: I(n,n−2)=(2√{square root over (3)}−1)*3n−2.
If, instead, the bridges 170 are placed on level n−1, the top level Y 108 can be removed, as shown in
Again, the maximum incremental benefit is provided at level n−2. Accordingly, for Y-tree architecture with or without the dead cells 178, it is preferred that level m=n−2 is used for a location of the bridges to provide the best derivative benefit for the bridges. Imax=3n−2(2√{square root over (3)}−1) for the architecture with dead cells 178, and Imax=3n−2(2√{square root over (3)}+3) for the architecture without dead cells.
Thus, in an exemplary implementation of the cost function and derivative benefit, it can be determined that, when adding the bridges 170 to X, H, and Y tree structures, the incremental improvement connecting nodes at 2, 3, and 2 levels, respectively, below the root are optimal.
It is advantageous for chip design optimization to focus on the interconnect resources. In the future, significantly greater numbers of routing layers (for example, twelve or more) will be available in high performance circuit designs. Thus, it is desirable to consider various ways to organize on-chip routing resources. However, the prohibitive cost of actually designing and manufacturing a chip with new interconnect architectures makes it difficult to implement and test new interconnect architectures individually. Thus, it is highly desirable to develop a quantitative framework to evaluate the efficiency of different interconnect architectures.
In prior methods of evaluating efficiency, the interconnect length reduction was studied by allowing more routing directions, but all of these methods concentrated on the Steiner cost of a single signal net. Competition over routing resources between different nets is typically ignored using these methods.
According to another aspect of the present invention, an assessment method for determining a benefit of a particular structure is provided. This method adopts a multi-commodity flow (MCF) approach to model the on-chip communication traffic. MCF is a natural way to model communication network traffic. For example, MCF has been used to study wide area communication network traffic. However, due to the high computing complexity of MCF, most uses of this approach adopt heuristic methods to approximate an MCF solution.
A preferred embodiment of the present assessment method extends the MCF algorithm to solve various MCF problems and provides improved chip routing design methods. Solution of MCF finds the optimal throughput for a given routing architecture.
According to a preferred method of the present invention, stated generally, a mesh structure is assumed having uniform communication demand; that is, the routing demand is equal for every pair of nodes. The MCF throughput of the mesh structure is used to measure communication capability of different interconnect architectures. This method is independent of particular test cases, and is independent of placement and routing. The extended MCF according to a preferred assessing method can reflect the exact communication bottlenecks on the chip or network, and it can provide a feasible upper bound of communication.
Algorithms involving this type of MCF can be solved fairly efficiently using, for example, the methods described in N. Garg and J. Koneman, “Faster and Simpler Algorithms for Multicommodity Flow and other Fractional Packing Problems,” In Proc. Of the 39th Annual Symposium on Foundations of Computer Science, pp. 300-309, 1998.
Turning now to an exemplary assessment method,
(1) Each slot 180 i corresponds to the node 186 i in the graph.
(2) The adjacency between two slots 182 (i, j) is represented by an edge 184 e=(i, j) in the graph.
(3) The edge capacity c (e) is proportional to the length of the line segment separating the adjacent slots 182, and the number of routing layers.
A uniform communication requirement is assumed; that is, every pair of nodes 186 communicates with an equal demand. All communications are assumed to happen at the same time. The model can be extended to various other communication demands as well such as, but not limited to, Poisson distribution, Rents rule, etc., depending on specific applications. For simplicity and for generalness, the example of uniform pairwise communication is adopted for the description herein. Uniform pairwise communication demand also provides an unbiased symmetry, which makes the solution independent of the test cases, placement, and routing.
Throughput, z, is defined to be maximum amount of communication flow between every pair of nodes 186. The throughput is determined using a MCF model. The flow that starts from node i is defined as “commodity” i. Commodity i starts from node 186 i with the amount of z (N−1), where N=n2 is the number of nodes in the graph, to each of the rest of the nodes with the amount of z. The MCF problem is solved to find the maximum throughput z.
The above MCF problem can be formulated as a linear program in either the node-arc form (LP1), or the edge-path form (LP2). The node-arc form (LP1) of MCF is:
In this linear program flow variable fvij represents the flow amount of commodity v on edge 184 (i, j). The edge capacity cij represents the flow capacity of edge 184 (i, j), in a uniform mesh using X-architecture, and cij is set to be unitary for all (i, j). The flow injecting to a node 186 is set to be positive and the flow ejecting from a node is set to be negative.
This linear program includes two sets of constraints. The first constraint describes the flow conservation of each commodity v at each node 186 i. The second constraint denotes that the total amount of flow on each edge 184 is no more than the capacity of that edge.
The edge-path form of MCF (LP2) is as follows:
In linear program LP2, Pe denotes the set of all paths p containing the edge 184 e, and Pij denotes the set of all paths between nodes 186 i, j. The flow variable f(p) represents the flow amount of path p.
The number of linear constraints in linear program LP1 is |V|2+|E|. Thus, the linear program LP1 can be solved in polynomial time using any polynomial time linear program solver, such as that disclosed in N. Karmarkar, “A new polynomial-time algorithm for linear programming,” Combinatorica, 4(4):373-395, 1984. However, when n increases, the number of linear constraints significantly increases (at the rate of n4 for an n×n mesh). Thus, for large cases, it may be impractical to solve the MCF using linear programming.
A combinatorial (1+ε)-approximation approach has been proposed to solve the MCF problem. An example of this combinatorial approach is disclosed in N. Garg and J. Konemann, “Faster and Simpler Algorithms for Multicommodity Flow and other Fractional Packing Problems,” In Proc. of the 39th Annual Symposium on Foundations of Computer Science, pp, 300-309, 1998.
In an embodiment of the present invention, the approach of this approximation algorithm is extended to incorporate edge capacities as variables. This approach adopts the primal-dual structure of the linear program LP2.
Generally stated, a preferred algorithm according to the present invention assigns a nonnegative shadow cost to each edge 184, according to the congestion level at that edge. Initially, all of the shadow costs are set to be equal. Then, the algorithm proceeds in iterations. In each iteration, a fixed amount of flow is rerouted along the shortest path for every commodity. At the end of each iteration, the capacity of every edge 184, and its shadow cost, is adjusted according to the dual linear program.
For every given error tolerance ε, a preferred embodiment of this MCF algorithm can find a (1+ε) approximation of the throughput in
In a preferred embodiment of the approximation method, all fractional flows are used. The throughput, {circumflex over (z)}, of the fractional flow model, is an upper bound of the throughput, {tilde over (z)} of the integer flow model. However, networks such as a packet switching network in RAW and Smart Memories, do not require that the flow be an integer. For wire switching networks in FPGA's, the flow amounts can be interpreted as the number of wires, which need to be integers.
In R. Motwani and P. Raghavan, Randomized Algorithms, Cambridge University Press, 1995, pp. 79-83, it was shown that by randomized rounding, with the probability of 1−ε, one can find {circumflex over (z)} approaches {tilde over (z)} with inequality {circumflex over (z)}≧{tilde over (z)}/(1+Δ30 (1/{circumflex over (z)}, ε/2N)), where N is the number of nodes in the mesh, ε is any real number between 0 and 1, and Δ+(1/{circumflex over (z)}, ε/2N) is the value of δ such that
[eδ/(1+δ)1+δ]1/{circumflex over (z)}=ε/2N.
The MCF algorithm described above will now be used by example to compare throughput of a number of different mesh structures: the 90° mesh 180, a 45° mesh 190, and the 90° and 45° mixed mesh 192. Results show that the 45° mesh 190 can achieve better throughput than the 90° mesh 180. Moreover, 90° and 45° mixed mesh 192 can further improve throughput.
In a first set of examples of a preferred assessment method, a number of routing algorithms are constructed having different capacities and routing orientations. The first three structures are 90° meshes 180 with different edge capacities. In the first architecture, every edge 184 has a unitary capacity. In the second architecture, edges 184 on the same row or column have equal capacity. In the third architecture, edge capacities are flexible, but the sum of the capacities of all of the edges 184 is fixed. The fourth architecture is a 45° mesh 190 where interconnections are routed at 45° angles. The fifth architecture is a mixture of 90° and 45° mesh 182. The sixth architecture is a mixed 90° and 45° mesh 192 with different routing direction assignments.
For the model of uniform edge capacity, all the edge capacity is set to a unit, that is, cij=1 for all edges 184 (i,j) in the graph. This case is used as a basis. It is assumed that the n×n array of slots 182 is evenly distributed in a square area.
In the second interconnection structure, edge capacities cij are set as variables. However, the capacities of edges 184 in the same row are set to be equal. Likewise, the vertical capacities of edges 184 in the same column are set to be equal. The sum of the vertical edge capacities in a row is set to be n, and the sum of the horizontal edge capacities in a column is set to be n. In other words, the height and width of the array remain n.
Let cHi be the capacity of horizontal edges 184 in the i-th row, and cVi be the capacity of vertical edges in the i-th column. We add the 2n variables, cH1, cH2, . . . , cHm, cV1, cV2, cVm, to the linear program. The height and width constraints of the array can be expressed as:
For this structure, it is assumed that one can adjust the row height and the column width of the array of processors.
For the third structure we give the program more freedom to choose the best edge capacities. We require only that the total capacity of all edges be a constant. This structure represents the best edge capacity we can allocate for a 90° mesh. The resultant throughput is an upper bound of a 90° mesh architecture.
We set the edge capacities, cij, as variables. The total capacity constraint is expressed as:
Note that 2·(n2−n) is the number of edges 184 in an n×n mesh. For this structure, we assume that the area of each slot 182 is flexible. We adjust the height and width of each individual slot so that the total area remains the same.
The fourth structure adopts the 45° mesh 190. All interconnects are oriented in 45° or 135° directions. The size of the mesh 190 increases with n. For a 45° mesh 190 of n, the number of nodes 186 is n2+(n−1)2, and the number of edges 184 is 4 (n−1)2.
In the fifth structure, we add diagonal edges; that is, 45° edges and 135° edges, to the 90° mesh 180 of Manhattan architecture to form the structure represented by the communication graph shown in
As shown in
In other words, for a pair of routing layers, if a capacity of x can be allocated to the rectilinear edges 184, only a capacity of x/√{square root over (2)} can be allocated to the diagonal edges. If we let c1 be the capacity of the rectilinear edges 184 and c2 be the capacity of the diagonal edges, the area constraints can be expressed as c1+√{square root over (2)}c2=1. In this way, the total area is equal to the constant area of uniform structure.
The above routing area constraint can be added into the linear programs LP1 or LP2, treating the edge capacities as variables. The optimal solution of the linear program produces an optimal routing resource allocation for different routing directions. The routing resource allocation problem can be formally formulated in the following way:
Input: communication graph G=(V, E), k different routing channels {R1, . . . , Rk}, where
edge capacity c1 for every edge in the routing channel Ri and area constraints
Output: a routing resource allocation {ci}, such that the communication graph G={V, E} has maximum throughput.
The routing resource allocation problem can be written as the following linear program:
This linear program finds the minimum routing area that can satisfy the unit pairwise communication demand. The dual program of this linear program is:
The dual program assigns a nonnegative shadow cost de to each edge 184 e, such that the sum of the shortest distances between every distinct pair of nodes 186 is maximized. The constraints in the above equations denote that the total shadow costs of all edges 184 in a routing channel are smaller than or equal to the area coefficient of that routing channel.
By extending the combinatorial (1+ε)-approximation scheme as described above, the routing resource allocation problem can be solved. In a preffered method, a shadow cost is determined by the flow congestion level on each edge 184. Let g(e)=(f(e))/(ce) be the congestion level of edge 184 e, where f(e) is the total flow amount going through edge e, and ce is the capacity of e. The shadow cost d(e) is computed using:
constant related to desired approximation error ε.
Initially, all of the shadow costs are set to be equal. Then, the algorithm proceeds in iterations. In each iteration, a fixed amount of flow is rerouted along the shortest path for every commodity. At the end of each iteration, the capacity of every edge 184 and its shadow cost is adjusted according to the dual linear program.
The assessment algorithm will now be used to compare the Manhattan architecture, the Y-architecture, and the X-architecture for both rectangular and symmetrical chip designs. Vias 132 become an important concern when the number of routing layers increases. An embodiment of the present invention provides a network flow model that considers the vias 132. The basic assumption made is that each via 132 will block one routing track. For each slot 182, we set an upper bound on the total number of vias 132 and interconnects across the node 186.
For example, suppose there are k routing layers. Each slot 182 is now represented by k routing cells as shown in
To assess performance of the above-described MCF method, we used Matlab's linear program package on a Sun Ultra10 workstation to compute MCF solutions. For a case with 100 nodes, the run time exceeds 24 hours. We then implemented the MCF algorithm and the above-described routing resource allocation algorithm using C programming language. The implementation derived the MCF solutions for cases with up to 289 nodes within 12 hours.
Using the present routing resource algorithm, we compared the throughput of n×n meshes 210 using Manhattan architecture, Y-architecture, and X-architecture.
For an n×n mesh with Y-architecture, there are 3n2−4n+1 edges; for an n×n mesh 210 with Manhattan architecture, there are 2n2−2n 0° and 90° edges; and for an n×n mesh with X-architecture, there are 2n2−2n edges on 0° or 90° edges and 2(n−1)2 edges in the 45° or 135° direction. To fairly compare the throughput of meshes with different interconnect architectures, the same amount of routing resources should be allocated to meshes having the same size.
The throughput is 1/n when n is odd and (n2−1)/n3 when n is even.
The throughput is limited by edges 184 on the middle column and row. When n is an even number, edges in the central row and column form the bottleneck of the flow. When n is an odd number, the two columns and two rows form the bottleneck.
For example, for equal n, the throughput of a 90° mesh with uniform row and column capacities is exactly the same as that of the 90° mesh with fixed edge capacities. No throughput improvement is obtained because the total capacity of the edges in each column and row is fixed.
For n=2 to 10,
The results also show that all edges 184 are congested. The optimal edge capacity is no longer uniform. The capacity is larger for the edges in the middle row and middle column.
As shown in
At least the following observations can be made with regard to
The throughput of the mixed mesh 192 is better than the 90° mesh 180, given the equal communication resource. The improvement in the throughput is up to 20.04% for a large number of nodes. The improvement is also better than 45° mesh 190 in terms of throughput.
With n increasing, the optimal ratio for the capacity of the 45° edge to the 90° edge approaches 5.6.
Using the MCF model in
In an exemplary comparison, the sum of all edge capacities is set to be equal to 2n2−2n for all n×n meshes, and the routing resource algorithm is used to find the optimal allocation of edge capacities.
As shown in
For example, for Manhattan architecture, there are n edges 184 crossing each cut line. The total edge capacity is n. For Y-architecture, there are 2n−1 edges 184 passing across each cut line 214, and each edge has capacity ⅔, so that the total edge capacity crossing the cut line is (4n−2)/3. When n approaches infinity, an n×n mesh using Y-architecture can have (4/3−1)−33.3% more flow crossing the cut line 214. Thus, Y-architecture can achieve up to 33.3% throughput improvement over Manhattan architecture on a squared mesh.
For X-architecture, there are 2(n−1) diagonal edges and n Manhattan edges crossing each of the two cut lines 214. To achieve maximum throughput, the ratio of the capacity for diagonal edges and the capacity for Manhattan edges is 5.6. Under this ratio, the edge capacities are 0.1515 and 0.6 for the Manhattan edges and diagonal edges respectively. The total flow amount that can go across the cut line is 1.3535n−1. When n approaches infinity, the throughput improvement bound is thus 35.6%.
For all of the cases that have been tested (n=2 to 17), these kind of central horizontal cut sets were observed using X-, Y-, and Manhattan architectures. Furthermore, in all of these cases, there is no flow passing through the same cut set more than once. If this is true for all n×n meshes, the improvement upper bounds derived are exact throughput improvement rates.
The same analysis was performed on symmetrical chip shapes as described above. A rectangular chip has communication bottlenecks on its respective two middle cut lines. The physical dimension of the middle part of the chip restricts the communication flow, and thus prevents larger throughput. Using a convex-shaped chip, better throughput is possible by allowing more wires to cross the original middle cut lines. This is verified using an embodiment of the routing algorithm of the present invention.
As shown in
Using the above-described routing algorithm, throughput of the symmetrical structures 230, 232, 234 for the Y-architecture, X-architecture, and Manhattan architecture was computed.
As shown, for Y-architecture, a hexagonal mesh 230 with 169 nodes, for example, produces 17.3% more throughput than a 13×13 rectangular mesh using the same interconnect architecture. For X-architecture, an octagonal mesh with 101 nodes, for example, can achieve 13.4% more throughput than a 10×10 rectangular mesh, which has 100 nodes. For Manhattan architecture, a diamond-shaped mesh 234 with 265 nodes, for example, provides a throughput of 5.61e−4, while a 16×16 mesh using the same interconnect architecture, which has 256 nodes, produces a throughput of 4.88e−4, so that a throughput of diamond mesh 234 over square mesh for Manhattan architecture is determined to be 15%.
As shown in
The following exemplary benefits are thus revealed via the MCF algorithm of a preferred embodiment of the present invention:
For uniform capacity mesh, the congested edges 212 lie in the center rows and columns. The total throughput of each node 186 is inversely proportional to the dimension of the mesh.
The re-arrangement of capacities between different columns or rows will not improve the throughput if the total capacity of the columns or rows is kept constant.
A flexible chip shape provides a throughput improvement of about 30% over a square chip of equal area.
A 45° mesh structure 190 produces about 17% more throughput over a 90° mesh 180 for a processor array of 144 nodes.
A mixture of 90° and 45° mesh structures 192 can achieve an additional 30% throughput To achieve maximum throughput, the ratio of resources allocated to the 45° routing layers versus those to the 90° routing layers approaches 5.6 as the number of nodes 186 increases.
In the 90° and 45° mixed routing, interleaving the diagonal routing layer and the Manhattan routing layers can reduce the number of vias and hence increase the communication throughput.
Interconnect length has a significant impact on virtually every-important measure of chip quality. From the physical point of view, decreasing interconnect length directly reduces the resistance and capacitance of the interconnect, thus improving the performance and power consumption of the circuits. From a designer's point of view, shorter total interconnect length produces less routing congestion on the chip, and therefore improving the routability and signal integrity of the design. At the same time, from a manufacturing perspective, shortening the interconnect length can improve the manufacturability and reliability of the chip.
Because of its highly limited freedom for choosing routing directions, Manhattan architecture adds a significant amount of interconnect length versus the Euclidean optimum. Allowing more routing directions has been found to shorten the total interconnect length. Previously, researchers have studied the impact of using different interconnect architecture on the interconnect length. Many of these efforts have involved constructing the Steiner routing trees under different routing direction restriction. However, due to the inherent difficulty of the Steiner minimum tree problem, a significant amount of time has been spent developing heuristics for construction Steiner trees for a randomly generated net, and for statistically calculating the average interconnect length for different interconnect architectures.
An additional embodiment of the present invention derives a quantitative comparison of interconnect lengths needed to connect a two pin net using different interconnect architectures. To generalize the non-rectilinear routing structure, the concept of λ-geometry has been introduced. λ represents a number of possible routing directions. In λ-geometry, interconnects with angles iπ/λ, for all i are allowed, where λ is a positive integer. λ=2, 3, 4 correspond to the Manhattan architecture, Y-architecture, and X-architecture, respectively.
The derivation adheres to the following rules:
(1) In λ-geometry, given two points A and B, if AB are not on any of the x feasible routing directions, then the shortest path connecting AB consists of two segments AC and CB, where the angle between AC and CB is (1−1/λ)π.
(2) Let A, B be any two points on the place, re be the Euclidean distance between A and B, and rλ be the length of the shortest interconnect to connect AB in λ-geometry, then
(3) Let A, B be two random points on the plane, re be the expected Euclidean distance between A and B, and rλ be the expected length of the shortest interconnect to connect AB in λ-geometry, then
Rule (1) provides that, in order to connect two pins with the shortest interconnect, there is at most one turn on the path, and it is desirable to maximize the angle between two segments of the path for the given interconnect architecture. For different interconnection architectures, Rule (2) determines the worst-case amount of additional interconnect length cost versus the Euclidean distance. For example, for Manhattan architecture, in the worst case, the interconnect length is 41.2% longer that the Euclidean distance. For Y-architecture and X-architecture, respectively, the additional interconnect length is at most 15.47% and 8.23%.
Rule (3) determines the average interconnect length of a two pin net using different interconnection architectures. For Manhattan architecture, the average interconnect length is 27.32% longer than its Euclidean distance. For Y-architecture, the average interconnect length is 10.27% longer than its Euclidean distance. The X-architecture further reduces the average interconnect length to be within 5.48% of the Euclidean optimum and it produces 4.3% interconnect length reduction over Y-architecture, but with the added cost of one more routing direction.
A novel non-blocking hierarchical interconnect architecture, Y-architecture, has been shown and described herein. The hexagonal cell arrays employed in Y-architecture have the property of hierarchical expansion and therefore nonblocking hierarchical interconnect architectures can be set up on them. According to an objective function also provided herein to balance interconnects resources and performance, it is shown that Y-architecture preferably is only 7% less effective than X-architecture. Because the distribution of hexagonal cells has the same pattern as that of the base stations of wireless communication systems, the architecture provided herein can also be used to optimize wireless systems, for example.
While various embodiments of the present invention have been shown and described, it should be understood that other modifications, substitutions, and alternatives are apparent to one of ordinary skill in the art. Such modifications, substitutions, and alternatives can be made without departing from the spirit and scope of the invention, which should be determined from the appended claims.
Various features of the invention are set forth in the appended claims.
Applicants claim priority benefits under 35 U.S.C. § 119 on the basis of Patent Application No. 60/410,011, filed Sep. 10,2002 and Application No. 60/410,396, filed Sep. 11, 2002.
This invention was made with Government assistance under NSF Grant No. 9987678. The Government has certain rights in this invention.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US03/28620 | 9/9/2003 | WO | 00 | 3/1/2005 |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2004/025734 | 3/25/2004 | WO | A |
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| Number | Date | Country | |
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| 20060049468 A1 | Mar 2006 | US |
| Number | Date | Country | |
|---|---|---|---|
| 60410011 | Sep 2002 | US | |
| 60410396 | Sep 2002 | US |
