This application is a Non-Provisional of U.S. Provisional Patent Application 62/059,488 filed on Oct. 3, 2014, which is incorporated herein by reference.
The present disclosure relates generally to semiconductor memory and methods, and more particularly, to multidimensional contiguous virtual memory allocation.
Memory devices are typically provided as internal, semiconductor, integrated circuits in computing systems or other electronic devices. There are many different types of memory including volatile and non-volatile memory. Volatile memory can require power to maintain its data (e.g., user data, error data, etc.) and includes random-access memory (RAM), dynamic random access memory (DRAM), and synchronous dynamic random access memory (SDRAM), among others. Non-volatile memory can provide persistent data by retaining stored data when not powered and can include non-volatile random access memory (NVRAM), NAND flash memory, NOR flash memory, read only memory (ROM), Electrically Erasable Programmable ROM (EEPROM), Erasable Programmable ROM (EPROM), and resistance variable memory such as phase change random access memory (PCRAM), resistive random access memory (RRAM), and magnetoresistive random access memory (MRAM), such as spin torque transfer random access memory (STT RAM), among others.
Computing systems often include a number of processing resources (e.g., one or more processors), which may retrieve and execute instructions and store the results of the executed instructions to a suitable location. A processing resource can comprise a number of functional units (e.g., herein referred to as functional unit circuitry (FUC)) such as arithmetic logic unit (ALU) circuitry, floating point unit (FPU) circuitry, and/or a combinatorial logic block, for example, which can execute instructions to perform logical operations such as AND, OR, NOT, NAND, NOR, and XOR logical operations on data (e.g., one or more operands).
A number of components in a computing system may be involved in providing instructions to the functional unit circuitry for execution. The instructions may be generated, for instance, by a processing resource such as a controller and/or host processing resource. Data (e.g., the operands on which the instructions will be executed to perform the logical operations) may be stored in a memory array that is accessible by the FUC. The instructions and/or data may be retrieved from the memory array and sequenced and/or buffered before the FUC begins to execute instructions on the data. Furthermore, as different types of operations may be executed in one or multiple clock cycles through the FUC, intermediate results of the operations and/or data may also be sequenced and/or buffered. In many instances, the processing resources (e.g., processor and/or associated FUC) may be external to the memory array, and data can be accessed (e.g., via a bus between the processing resources and the memory array to execute instructions). Data can be moved from the memory array to registers external to the memory array via a bus.
Virtual memory is a memory mapping technique that is implemented using both hardware and software. It maps memory addresses used by a program, called virtual addresses, into physical addresses in physical memory. The memory space, as seen by a process or task, appears as contiguous address space or a collection of contiguous segments. An operating system manages virtual address spaces and the assignment of physical memory to virtual memory. Software within the operating system may extent these capabilities to provide a virtual address space that can exceed the capacity of real memory and thus reference more than is physically present. Some benefits of virtual memory include freeing applications from having to manage a shared memory space, increased security due to memory isolation, and being able to conceptually use more memory than might be physically available.
In some approaches to managing virtual memory, the virtual address space may appear to address physical memory as a collection of contiguous segments (e.g., one segment for each allocation of memory) that is one-dimensional, even if the physical memory is organized as a series of hierarchical multidimensional units. As such, virtual allocations can be fulfilled in virtually contiguous, physically disjoint strips of one-dimensional space. The physical memory may become fragmented, which inefficiently utilizes the memory and does not facilitate effective use of contiguous regions of the memory, particularly in more than one dimension of the memory. The more the physical memory becomes fragmented, the more difficult it may be to allocate larger contiguous portions of the memory. However, it may not be advantageous to have a direct correlation between the virtual address space and the physical address space. This can allow the operating system to work with different types of memory, different sizes of memory, and different memory protocols without having to significantly (or at all) change the way the virtual address space operates. For example, this can allow an application that is using virtual memory space to be assigned different physical memory space without having to change the virtual memory space assigned to the application (e.g., the application can be physically “relocated” without the knowledge of and/or action from the application).
Recent advances in memory technology, for example, related to processing in memory, which can benefit from multidimensional bit contiguity, may make it advantageous to allocate physically bit contiguous blocks of virtual memory. However, the notions of multidimensional physical bit contiguity do not fit current models of one-dimensional virtual allocation. Multidimensional bit contiguity is described in more detail with respect to
The present disclosure is related to multidimensional contiguous memory allocation. Multidimensional contiguous memory allocation can include receiving an allocation request for an amount of memory that is contiguous in multiple dimensions of the memory and determining whether the memory includes a region corresponding to the requested amount that is a candidate as being unallocated based on information indicating a maximum number of contiguous unallocated allocable portions of the memory. In response to determining that the memory includes the candidate region, the method can include determining whether the candidate region is unallocated based on a subset of information indicating whether each allocable portion of the memory is allocated. The subset of information corresponds to the candidate region only.
A number of embodiments of the present disclosure can provide the ability to explicitly allocate virtual memory in well-defined, contiguous multidimensional bit patterns without traversing all possible bits of the target physical memory to determine where space is available. Some embodiments can reduce the search space for a given allocation to be significantly less than the total number of bits in the target memory. A number of embodiments can reduce relative memory fragmentation in multiple dimensions across multiple devices when allocating virtual memory of different shapes and complexity.
In the following detailed description of the present disclosure, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration how a number of embodiments of the disclosure may be practiced. These embodiments are described in sufficient detail to enable those of ordinary skill in the art to practice the embodiments of this disclosure, and it is to be understood that other embodiments may be utilized and that process, electrical, and/or structural changes may be made without departing from the scope of the present disclosure. As used herein, the designators “A”, “B”, “C”, “M”, “N”, “S”, and “X”, particularly with respect to reference numerals in the drawings, indicates that a number of the particular feature so designated can be included. As used herein, “a number of” a particular thing can refer to one or more of such things (e.g., a number of memory devices can refer to one or more memory devices). As used herein, the terms “first” and “second” are used to differentiate between one feature from another and do not necessarily imply an order between the features so designated.
The figures herein follow a numbering convention in which the first digit or digits correspond to the drawing figure number and the remaining digits identify an element or component in the drawing. Similar elements or components between different figures may be identified by the use of similar digits. For example, 662 may reference element “62” in
The computing system 100 can include a host 102 coupled to memory system 104, which includes a memory device 110 (e.g., including a memory array 111 and/or sensing circuitry 124). The host 102 can be a host system such as a personal laptop computer, a desktop computer, a digital camera, a mobile telephone, or a memory card reader, among various other types of hosts. In some embodiments, the host 102 can be or include a memory management unit. A memory management unit is a hardware component that performs translation between virtual memory addresses and physical memory addresses. The host 102 can store and/or access a multidimensional matrix 116 (e.g., “ALLOC MATRIX”) and a number of vectors 114 such as “VECTOR 1” 114-1 and “VECTOR 2” 114-2. The host 102 can include a system motherboard and/or backplane and can include a number of processing resources (e.g., one or more processors, microprocessors, or some other type of controlling circuitry). The computing system 100 can include separate integrated circuits or both the host 102 and the memory system 104 can be on the same integrated circuit. The computing system 100 can be, for instance, a server system and/or a high performance computing system and/or a portion thereof. Although the example shown in
For clarity, the computing system 100 has been simplified to focus on features with particular relevance to the present disclosure. The memory array 111 can be a hybrid memory cube (HMC), processing in memory random access memory (PIMRAM) array, DRAM array, SRAM array, STT RAM array, PCRAM array, TRAM array, RRAM array, NVRAM array, NAND flash array, and/or NOR flash array, for instance. The memory array 111 can comprise memory cells arranged in rows coupled by access lines (which may be referred to herein as word lines or select lines) and columns coupled by sense lines (which may be referred to herein as digit lines or data lines). Although a single memory device 110 is shown in
The memory device 110 can be a multidimensional random access memory. A dimension of memory is a coordinate that can be used to specify a location within the memory (e.g., the location of a memory cell or allocable portion of memory). Examples of dimensions of a memory include rows, columns, layers (e.g., in the case of a hybrid memory cube), banks, chips, etc. A memory can have more than three dimensions in terms of coordinates. For example, a memory device 110 can include multiple memory channels (a first dimension of the memory device is a channel), each channel including multiple memory dies (a second dimension of the memory device 110 is a die), each die including multiple subarrays (a third dimension of the memory device 110 is a subarray), and each subarray including multiple rows (a fourth dimension of the memory is a row). Some embodiments are described herein with respect to a two-dimensional memory device for ease of illustration and explanation however embodiments are not so limited. One of ordinary skill in the art, having read and understood the present disclosure can apply the teachings to a memory device 110 having more than two dimensions.
The memory system 104 can include address circuitry 126 to latch address signals provided over an I/O bus 138 (e.g., a data bus) through I/O circuitry 130. Address signals can be received and decoded by a row decoder 128 and a column decoder 134 to access the memory device 110. Data can be read from the memory array 111 by sensing voltage and/or current changes on the sense lines using sensing circuitry 124. The sensing circuitry 124 can read and latch a page (e.g., row) of data from the memory array 111. The I/O circuitry 130 can be used for bi-directional data communication with host 102 over the I/O bus 138. The write circuitry 132 can be used to write data to the memory device 110.
Controller 108 can decode signals provided by control bus 136 from the host 102. These signals can include chip enable signals, write enable signals, and address latch signals that are used to control operations performed on the memory device 110, including data read, data write, and data erase operations. In various embodiments, the controller 108 is responsible for executing instructions from the host 102. The controller 108 can be a state machine, a sequencer, a processor, and/or other control circuitry.
An example of the sensing circuitry 124 is described further below in association with
In various previous approaches, data associated with a logical operation, for instance, would be read from memory via sensing circuitry and provided to an external ALU. The external ALU circuitry would perform the logical operations using the elements (which may be referred to as operands or inputs) and the result could be transferred back to the array via the local I/O lines. In contrast, in a number of embodiments of the present disclosure, sensing circuitry 124 can be configured to perform a logical operation on data stored in memory cells in memory array 111 and store the result back to the array 111 without enabling a local I/O line coupled to the sensing circuitry.
As such, in a number of embodiments, registers and/or an ALU external to the memory array 111 and sensing circuitry 124 may not be needed to perform the logical operation as the sensing circuitry 124 can be operated to perform the logical operation using the address space of memory array 111. Additionally, the logical operation can be performed without the use of an external processing resource.
The host 102 can be configured with an operating system “OS” 112. The host 102 can be coupled to the memory device 110 (e.g., via the control bus 136 and/or the I/O bus 138. The OS 112 is executable instructions (software) that manages hardware resources and provides services other executable instructions (programs) that run on the OS 112. The OS 112 can implement a virtual memory (VM) system 123. The VM system 123 can control the vectors 114 and the matrix 116. The VM system 123 can implement a search algorithm 118 to search the vectors 114 and/or scan the matrix 116. For example, the OS 112 can include instructions to search a number of vectors 114, each corresponding to a dimension of the memory device 110, instructions to scan the matrix 116 indicating whether respective allocable portions of the memory device 110 are allocated, instructions to allocate 122-1 and/or deallocate 122-2 memory, and/or instructions to manage the virtual memory system 123, among other instructions. The OS 112 can store and/or access a multidimensional matrix 116 and a number of vectors 114. In some embodiments, a vector can be a one-dimensional array.
The OS 112 can include instructions to respond to a received memory allocation request. The memory allocation request can be a request for an amount of memory that is contiguous in a plurality of dimensions of the memory, as described herein. Memory allocation requests can originate from the host 102 (e.g., from a program running on the host 102) among other originations (e.g., from a direct memory access (DMA) device). The memory allocation requests can be for virtual memory and the OS 112 can include the instructions to manage the virtual memory 123 by mapping memory addresses (e.g., virtual addresses) used by a program to physical addresses in the memory device 110 and vice versa. Additional detail regarding such instructions is provided with respect to
As described in more detail herein, a multidimensional matrix that includes a respective plurality of units per dimension can be stored, where each unit indicates whether a respective allocable portion of the memory is allocated. Each dimension of the matrix can correspond to a respective dimension of the memory. An n-dimensional matrix corresponds to an n-dimensional memory. For example with respect to
Each column of memory cells can be coupled to sensing circuitry 324, which can be analogous to sensing circuitry 124 illustrated in
In a number of embodiments, the sensing circuitry (e.g., compute components 348 and sense amplifiers 346) is configured to perform a number of logical operations on elements stored in array 311. As an example, a first plurality of elements can be stored in a first group of memory cells coupled to a particular access line (e.g., access line 342-1) and to a number of sense lines 344, and a second plurality of elements can be stored in a second group of memory cells coupled to a different access line (e.g., access line 342-2) and the respective number of sense lines 344. Each element of the first plurality of elements can have a logical operation performed thereon with a respective one of the second plurality of elements, and the result of the logical operation can be stored (e.g., as a bit-vector) in a third group of memory cells coupled to a particular access line (e.g., access line 342-3) and to the number of sense lines 344.
A number of embodiments of the present disclosure can include allocating a region of memory that is contiguous in a plurality of dimensions. In the example described above, and as illustrated in more detail with respect to
Each vector 414 corresponds to a respective one of the dimensions 461 (e.g., vector 414-1 corresponds to dimension 416-1 and vector 414-2 corresponds to dimension 416-2). Each vector 414 includes a respective plurality of entries 468. Each entry 468 represents a maximum number of contiguous unallocated allocable portions 470 of the memory in the corresponding dimension that correspond to a particular element of a different dimension. For example, vector 414-1 includes a plurality of entries including entry 468-1 (e.g., “5”), which represents the maximum number of contiguous unallocated allocable portions 470-1 of the memory in the corresponding first dimension 461-1 that correspond to a particular element of the second dimension 461-2. The entry 468-1 (“5”) indicates that there are a maximum of 5 contiguous unallocated allocable portions (the units that are equal to “0” in the column of units above entry 468-1, which includes, in order, from top-to-bottom, units 0, 1, 1, 0, 0, 0, 0, 0, thus including a maximum of 5 consecutive zeroes). That column of units also includes a single zero by itself, but five is greater than one, which is why the entry 468-1 indicates “5”. Likewise, for example, the vector 414-2 includes a plurality of entries including entry 468-2 (e.g., “3”), which represents the maximum number of contiguous unallocated allocable portions 470-2 of the memory in the corresponding second dimension 461-2 that correspond to a particular element of the first dimension 461-1. The entry 468-2 (“3”) indicates that there are a maximum of 3 contiguous unallocated allocable portions (the units that are equal to “0” in the row of units to the left of entry 468-2, which includes, in order, from left-to-right, units 1, 1, 1, 0, 0, 0, 1, 1, thus including a maximum of 3 consecutive zeroes).
An operating system of a host and/or a controller can be configured to search the vectors 414 to determine whether the memory includes a candidate region corresponding to an amount of memory that is contiguous in the plurality of dimensions 461. The amount of memory can be defined by a received memory allocation request that specifies an amount of memory that is contiguous in more than one dimension 461. The operating system of the host and/or the controller can search the vectors 414 to determine whether the maximum number of contiguous unallocated allocable portions 470 of the memory per corresponding dimension 461 is sufficient in the dimensions 461 to fulfill the memory allocation request.
An operating system of a host (e.g., operating system 112 of host 102 illustrated in
An operating system of a host and/or a controller can be configured to determine whether the first dimension 461-1 includes a first number of contiguous unallocated allocable portions sufficient to fulfill the memory allocation request. For example, this can be accomplished by searching a vector 414-1 corresponding to the first dimension 461-1 for information indicating a maximum number of contiguous unallocated allocable portions of the physical memory in the first dimension 461-1. Each entry in the vector 414-1 indicates the maximum number of contiguous unallocated allocable portions of the physical memory in the first dimension 461-1. Because the memory allocation request specifies four contiguous allocable portions in the first dimension 461-1 and five contiguous allocable portions in the second dimension 461-2, the vector 414-1 can be searched for five consecutive entries that indicate at least four consecutive allocable portions are unallocated. The first three entries in the vector 414-1 are 3, 3, 3, so these will not be sufficient to fulfill the memory allocation request. However the remaining five entries are 8, 5, 5, 5, 5, which means that there are at least five consecutive entries greater than or equal to four, so the memory should have sufficient availability in the first dimension 461-1. These consecutive sufficient entries are labeled as 474-1 in
An operating system of a host and/or a controller can be configured to determine whether the second dimension 461-2 includes a second number of contiguous unallocated allocable portions sufficient to fulfill the memory allocation request. For example, this can be accomplished by searching a vector 414-2 corresponding to the second dimension 461-2 for information indicating a maximum number of contiguous unallocated allocable portions of the physical memory in the first dimension 461-2. Because the memory allocation request specifies four contiguous allocable portions in the first dimension 461-1 and five contiguous allocable portions in the second dimension 461-2, the vector 414-2 can be searched for four consecutive entries that indicate at least five consecutive allocable portions are unallocated. The first three entries in the vector 414-1 are 3, 1, 1, so these will not be sufficient to fulfill the memory allocation request. However the next four entries are 5, 5, 8, 8, which means that there are at least four consecutive entries greater than or equal to five, so the memory should have sufficient availability in the second dimension 461-2. These consecutive sufficient entries are labeled as 474-2 in
However, the candidate region 472-1 may be partially allocated (see the example illustrated in
Updating the vectors 514 can include calculating new entries for the sets (e.g., sets 474-1, 474-2 illustrated in
Updating the vectors 614 can include calculating new entries for the sets (e.g., sets 474-1, 474-3 illustrated in
Comparing the result of the allocation illustrated in
Because the memory allocation request specifies three contiguous allocable portions in the first dimension 761-1 and four contiguous allocable portions in the second dimension 761-2, the vector 714-1 can be searched for four consecutive entries that indicate at least three consecutive allocable portions are unallocated. The first four entries in the vector 714-1 are 3, 4, 3, 5, which means that there are at least four consecutive entries greater than or equal to three, so the memory should have sufficient availability in the first dimension 761-1. These consecutive sufficient entries are labeled as 774-1 in
Because the memory allocation request specifies three contiguous allocable portions in the first dimension 761-1 and four contiguous allocable portions in the second dimension 761-2, the vector 714-2 can be searched for three consecutive entries that indicate at least four consecutive allocable portions are unallocated. The first three entries in the vector 714-1 are 4, 1, 1, so these will not be sufficient to fulfill the memory allocation request. However, skipping down, the last three entries are 4, 4, 8, which means that there are at least three consecutive entries greater than or equal to four, so the memory should have sufficient availability in the second dimension 761-2. These consecutive sufficient entries are labeled as 774-2 in
The operating system of a host and/or a controller can be configured to scan a subset of information indicating whether each allocable portion of the memory is unallocated in response to determining that the first candidate region 772-1 exists. The subset corresponds to the first candidate region 772-1 only. The subset of information can be the information indicated in the matrix 716 within the area highlighted as the first candidate region 772-1. This information can be used to determine whether the first number of contiguous unallocated allocable portions (e.g., the at least three contiguous allocable portions searched for in the first vector 714-1) and the second number of contiguous unallocated allocable portions (e.g., the at least four contiguous allocable portions searched for in the second vector 714-2) are aligned in the first dimension 761-1 and the second dimension 761-2 to form a region of physical memory that is contiguous in the first dimension 761-1 and the second dimension 761-2. If they are aligned in the first dimension 761-1 and the second dimension 761-2, then the region can be allocated. However, as illustrated in
This is an example of an instance where the information in the vectors 714 is insufficient on its own to provide certainty whether the candidate region 772-1 is unallocated, which is why the subset of the matrix 716 is scanned after a candidate region 772-1 is identified. The candidate region 772-1 is partially allocated and thus not unallocated. If the candidate region 772-1 had been unallocated, it could have been allocated to fulfill the memory allocation request. In response to the subset of information indicating the first candidate region 772-1 as being allocated (at least partially allocated), the operating system of a host and/or a controller can be configured to determine whether a second region of the memory that is sufficient to fulfill the memory request and that is contiguous in more than one dimension and that is a candidate as being unallocated exists. A determination can be made as to whether the memory includes a second region that is contiguous in the plurality of dimensions 761 corresponding to the requested amount that is a second candidate, as illustrated in more detail in
As was previously described with respect to
The last four entries in the vector 714-1 are 3, 3, 3, 3, which means that there are at least four consecutive entries greater than or equal to three, so the memory should have sufficient availability in the first dimension 761-1. These consecutive sufficient entries are labeled as 774-3 in
The set of entries 774-2 and the set of entries 774-3 collectively define the second candidate region 772-2. The operating system of a host and/or a controller can be configured to scan a different subset of the information (different than the subset described with respect to
If the different subset of information corresponding to the second candidate region 772-2 had not indicated that it was unallocated, then the operating system of a host and/or a controller can be configured to determine whether a third region of the memory that is contiguous in more than one dimension 761 and that is a candidate as being unallocated exists in response thereto. The vectors 714 can be searched and respective portions of the matrix 716 can be scanned, as described herein, for each iteration of this process. This process can be repeated until all possibilities have been eliminated or until the memory allocation request has been fulfilled. If a region that is contiguous in the plurality of dimension 761 of the memory and that is unallocated is not converged on (e.g., if all possibilities are eliminated without fulfilling the memory allocation request) a null point can be returned in response. The null point can indicate to the operating system, for example, that there is not sufficient memory available to fulfill the memory allocation request.
A, B, and C can be considered as variables that each include a number of elements (e.g., bits) from 0 to n. Variable A in row 860-1, variable B in row 860-2, and variable C in row 860-3 are bit contiguous in one dimension (e.g., along each row) because the elements are arranged in order and are all contained in the same dimension (e.g., row). Variables A and C are also contiguous in a second dimension (e.g., column) because corresponding elements of each variable are located in the same column. For example A[0] is in the same column as C[0], A[1] is in the same column as C[1], etc. This second dimension of contiguity may also be referred to as locality. However, variable B is not contiguous in a plurality of dimensions with variables A and C because the corresponding elements are not located in the same columns. It can be said that variables A, B, and C are bit contiguous in one dimension, but not bit local.
As an example of a logical operation that can be performed in memory in order to illustrate some advantages of the present disclosure, consider an operation that uses the first row 860-1 and the second row 860-2 as input and stores a result of the logical operation performed on the first row 860-1 and the second row 860-2 in the third row 860-3. By way of example, each ith element of the first row 860-1 and the second row 860-2 can be added. An example of code for this operation is:
where A is the first row 860-1, B is the second row 860-2, C is the third row 860-3, and each row 860 contains elements from 0 to N, individually referred to as i.
In order to perform the logical operation in memory, each ith element should be physically aligned in memory. If they are not aligned, then the software runtime allocates a temporary row 878 and preemptively moves the data (e.g., the second row 860-2 from array portion 811-2 to array portion 811-1) in order to perform the alignment prior to performing the logical operation. Thus, the additional operations of creating a temporary row and moving the data (reading and writing) must be performed. This adds latency to the logical operation that is dependent on the amount of data to be moved and the bandwidth of the respective subarrays 858.
Although not specifically illustrated as such, a non-transitory computing system readable medium for storing executable instructions can include all forms of volatile and non-volatile memory, including, by way of example, semiconductor memory devices, DRAM, HMC, EPROM, EEPROM, flash memory devices, magnetic disks such as fixed, floppy, and removable disks, other magnetic media including tape, optical media such as compact discs (CDs), digital versatile discs (DVDs), and Blu-Ray discs (BD). The instructions may be supplemented by, or incorporated in, ASICs.
Although specific embodiments have been illustrated and described herein, those of ordinary skill in the art will appreciate that an arrangement calculated to achieve the same results can be substituted for the specific embodiments shown. This disclosure is intended to cover adaptations or variations of one or more embodiments of the present disclosure. It is to be understood that the above description has been made in an illustrative fashion, and not a restrictive one. Combination of the above embodiments, and other embodiments not specifically described herein will be apparent to those of skill in the art upon reviewing the above description. The scope of the one or more embodiments of the present disclosure includes other applications in which the above structures and methods are used. Therefore, the scope of one or more embodiments of the present disclosure should be determined with reference to the appended claims, along with the full range of equivalents to which such claims are entitled.
In the foregoing Detailed Description, some features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the disclosed embodiments of the present disclosure have to use more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.
Number | Name | Date | Kind |
---|---|---|---|
4380046 | Fung | Apr 1983 | A |
4435792 | Bechtolsheim | Mar 1984 | A |
4435793 | Ochii | Mar 1984 | A |
4727474 | Batcher | Feb 1988 | A |
4736439 | May | Apr 1988 | A |
4843264 | Galbraith | Jun 1989 | A |
4958378 | Bell | Sep 1990 | A |
4977542 | Matsuda et al. | Dec 1990 | A |
4992935 | Comerford | Feb 1991 | A |
5023838 | Herbert | Jun 1991 | A |
5034636 | Reis et al. | Jul 1991 | A |
5201039 | Sakamura | Apr 1993 | A |
5210850 | Kelly et al. | May 1993 | A |
5253308 | Johnson | Oct 1993 | A |
5276643 | Hoffmann et al. | Jan 1994 | A |
5325519 | Long et al. | Jun 1994 | A |
5367488 | An | Nov 1994 | A |
5379257 | Matsumura et al. | Jan 1995 | A |
5386379 | Ali-Yahia et al. | Jan 1995 | A |
5398213 | Yeon et al. | Mar 1995 | A |
5440482 | Davis | Aug 1995 | A |
5446690 | Tanaka et al. | Aug 1995 | A |
5473576 | Matsui | Dec 1995 | A |
5481500 | Reohr et al. | Jan 1996 | A |
5485373 | Davis et al. | Jan 1996 | A |
5506811 | McLaury | Apr 1996 | A |
5615404 | Knoll et al. | Mar 1997 | A |
5638128 | Hoogenboom | Jun 1997 | A |
5638317 | Tran | Jun 1997 | A |
5654936 | Cho | Aug 1997 | A |
5678021 | Pawate et al. | Oct 1997 | A |
5724291 | Matano | Mar 1998 | A |
5724366 | Furutani | Mar 1998 | A |
5751987 | Mahant-Shetti et al. | May 1998 | A |
5787458 | Miwa | Jul 1998 | A |
5854636 | Watanabe et al. | Dec 1998 | A |
5867429 | Chen et al. | Feb 1999 | A |
5870504 | Nemoto et al. | Feb 1999 | A |
5915084 | Wendell | Jun 1999 | A |
5935263 | Keeth et al. | Aug 1999 | A |
5986942 | Sugibayashi | Nov 1999 | A |
5991209 | Chow | Nov 1999 | A |
5991785 | Alidina et al. | Nov 1999 | A |
6005799 | Rao | Dec 1999 | A |
6009020 | Nagata | Dec 1999 | A |
6092186 | Betker et al. | Jul 2000 | A |
6122211 | Morgan et al. | Sep 2000 | A |
6125071 | Kohno et al. | Sep 2000 | A |
6134164 | Lattimore et al. | Oct 2000 | A |
6147514 | Shiratake | Nov 2000 | A |
6151244 | Fujino et al. | Nov 2000 | A |
6157578 | Brady | Dec 2000 | A |
6163862 | Adams et al. | Dec 2000 | A |
6166942 | Vo et al. | Dec 2000 | A |
6172918 | Hidaka | Jan 2001 | B1 |
6175514 | Henderson | Jan 2001 | B1 |
6181698 | Hariguchi | Jan 2001 | B1 |
6208544 | Beadle et al. | Mar 2001 | B1 |
6226215 | Yoon | May 2001 | B1 |
6301153 | Takeuchi et al. | Oct 2001 | B1 |
6301164 | Manning et al. | Oct 2001 | B1 |
6304477 | Naji | Oct 2001 | B1 |
6389507 | Sherman | May 2002 | B1 |
6418498 | Martwick | Jul 2002 | B1 |
6466499 | Blodgett | Oct 2002 | B1 |
6480931 | Buti | Nov 2002 | B1 |
6510098 | Taylor | Jan 2003 | B1 |
6563754 | Lien et al. | May 2003 | B1 |
6578058 | Nygaard | Jun 2003 | B1 |
6731542 | Le et al. | May 2004 | B1 |
6754746 | Leung et al. | Jun 2004 | B1 |
6768679 | Le et al. | Jul 2004 | B1 |
6807614 | Chung | Oct 2004 | B2 |
6816422 | Hamade et al. | Nov 2004 | B2 |
6819612 | Achter | Nov 2004 | B1 |
6894549 | Eliason | May 2005 | B2 |
6943579 | Hazanchuk et al. | Sep 2005 | B1 |
6948056 | Roth | Sep 2005 | B1 |
6950771 | Fan et al. | Sep 2005 | B1 |
6950898 | Merritt et al. | Sep 2005 | B2 |
6956770 | Khalid et al. | Oct 2005 | B2 |
6961272 | Schreck | Nov 2005 | B2 |
6965648 | Smith et al. | Nov 2005 | B1 |
6985394 | Kim | Jan 2006 | B2 |
6987693 | Cernea et al. | Jan 2006 | B2 |
7020017 | Chen et al. | Mar 2006 | B2 |
7028170 | Saulsbury | Apr 2006 | B2 |
7045834 | Tran et al. | May 2006 | B2 |
7054178 | Shiah et al. | May 2006 | B1 |
7061817 | Raad et al. | Jun 2006 | B2 |
7079407 | Dimitrelis | Jul 2006 | B1 |
7173857 | Kato et al. | Feb 2007 | B2 |
7187585 | Li et al. | Mar 2007 | B2 |
7196928 | Chen | Mar 2007 | B2 |
7260565 | Lee et al. | Aug 2007 | B2 |
7260672 | Garney | Aug 2007 | B2 |
7372715 | Han | May 2008 | B2 |
7400532 | Aritome | Jul 2008 | B2 |
7406494 | Magee | Jul 2008 | B2 |
7447720 | Beaumont | Nov 2008 | B2 |
7454451 | Beaumont | Nov 2008 | B2 |
7457181 | Lee et al. | Nov 2008 | B2 |
7535769 | Cernea | May 2009 | B2 |
7546438 | Chung | Jun 2009 | B2 |
7562198 | Noda et al. | Jul 2009 | B2 |
7574466 | Beaumont | Aug 2009 | B2 |
7602647 | Li et al. | Oct 2009 | B2 |
7663928 | Tsai et al. | Feb 2010 | B2 |
7676627 | Wong | Mar 2010 | B2 |
7692466 | Ahmadi | Apr 2010 | B2 |
7685365 | Rajwar et al. | May 2010 | B2 |
7752417 | Manczak et al. | Jul 2010 | B2 |
7791962 | Noda et al. | Sep 2010 | B2 |
7796453 | Riho et al. | Sep 2010 | B2 |
7805587 | Van Dyke et al. | Sep 2010 | B1 |
7808854 | Takase | Oct 2010 | B2 |
7827372 | Bink et al. | Nov 2010 | B2 |
7869273 | Lee et al. | Jan 2011 | B2 |
7898864 | Dong | Mar 2011 | B2 |
7924628 | Danon et al. | Apr 2011 | B2 |
7937535 | Ozer et al. | May 2011 | B2 |
7957206 | Bauser | Jun 2011 | B2 |
7979667 | Allen et al. | Jul 2011 | B2 |
7996749 | Ding et al. | Aug 2011 | B2 |
8042082 | Solomon | Oct 2011 | B2 |
8045391 | Mokhlesi | Oct 2011 | B2 |
8059438 | Chang et al. | Nov 2011 | B2 |
8095825 | Hirotsu et al. | Jan 2012 | B2 |
8117462 | Snapp et al. | Feb 2012 | B2 |
8164942 | Gebara et al. | Apr 2012 | B2 |
8208328 | Hong | Jun 2012 | B2 |
8213248 | Moon et al. | Jul 2012 | B2 |
8223568 | Seo | Jul 2012 | B2 |
8238173 | Akerib et al. | Aug 2012 | B2 |
8274841 | Shinano et al. | Sep 2012 | B2 |
8279683 | Klein | Oct 2012 | B2 |
8310884 | Iwai et al. | Nov 2012 | B2 |
8332367 | Bhattacherjee et al. | Dec 2012 | B2 |
8339824 | Cooke | Dec 2012 | B2 |
8339883 | Yu et al. | Dec 2012 | B2 |
8347154 | Bahali et al. | Jan 2013 | B2 |
8351292 | Matano | Jan 2013 | B2 |
8356144 | Hessel et al. | Jan 2013 | B2 |
8417921 | Gonion et al. | Apr 2013 | B2 |
8462532 | Argyres | Jun 2013 | B1 |
8484276 | Carlson et al. | Jul 2013 | B2 |
8495438 | Roine | Jul 2013 | B2 |
8503250 | Demone | Aug 2013 | B2 |
8526239 | Kim | Sep 2013 | B2 |
8533245 | Cheung | Sep 2013 | B1 |
8555037 | Gonion | Oct 2013 | B2 |
8599613 | Abiko et al. | Dec 2013 | B2 |
8605015 | Guttag et al. | Dec 2013 | B2 |
8625376 | Jung | Jan 2014 | B2 |
8644101 | Jun et al. | Feb 2014 | B2 |
8650232 | Stortz et al. | Feb 2014 | B2 |
8873272 | Lee | Oct 2014 | B2 |
8964496 | Manning | Feb 2015 | B2 |
8971124 | Manning | Mar 2015 | B1 |
8996844 | Zhu | Mar 2015 | B1 |
9015390 | Klein | Apr 2015 | B2 |
9047193 | Lin et al. | Jun 2015 | B2 |
9165023 | Moskovich et al. | Oct 2015 | B2 |
20010007112 | Porterfield | Jul 2001 | A1 |
20010008492 | Higashiho | Jul 2001 | A1 |
20010010057 | Yamada | Jul 2001 | A1 |
20010028584 | Nakayama et al. | Oct 2001 | A1 |
20010043089 | Forbes et al. | Nov 2001 | A1 |
20020059355 | Peleg et al. | May 2002 | A1 |
20030023815 | Yoneyama | Jan 2003 | A1 |
20030167426 | Slobodnik | Sep 2003 | A1 |
20030222879 | Lin et al. | Dec 2003 | A1 |
20040073592 | Kim et al. | Apr 2004 | A1 |
20040073773 | Demjanenko | Apr 2004 | A1 |
20040085840 | Vali et al. | May 2004 | A1 |
20040095826 | Pemer | May 2004 | A1 |
20040154002 | Ball et al. | Aug 2004 | A1 |
20040205289 | Srinivasan | Oct 2004 | A1 |
20040240251 | Nozawa et al. | Dec 2004 | A1 |
20050015557 | Wang et al. | Jan 2005 | A1 |
20050078514 | Scheuerlein et al. | Apr 2005 | A1 |
20050097417 | Agrawal et al. | May 2005 | A1 |
20060047937 | Selvaggi et al. | Mar 2006 | A1 |
20060069849 | Rudelic | Mar 2006 | A1 |
20060146623 | Mizuno et al. | Jul 2006 | A1 |
20060149804 | Luick et al. | Jul 2006 | A1 |
20060181917 | Kang et al. | Aug 2006 | A1 |
20060215432 | Wickeraad et al. | Sep 2006 | A1 |
20060225072 | Lari et al. | Oct 2006 | A1 |
20060291282 | Liu et al. | Dec 2006 | A1 |
20070103986 | Chen | May 2007 | A1 |
20070171747 | Hunter et al. | Jul 2007 | A1 |
20070180006 | Gyoten et al. | Aug 2007 | A1 |
20070180184 | Sakashita et al. | Aug 2007 | A1 |
20070195602 | Fong et al. | Aug 2007 | A1 |
20070285131 | Sohn | Dec 2007 | A1 |
20070285979 | Turner | Dec 2007 | A1 |
20070291532 | Tsuji | Dec 2007 | A1 |
20070294179 | Krawetz | Dec 2007 | A1 |
20080025073 | Arsovski | Jan 2008 | A1 |
20080037333 | Kim et al. | Feb 2008 | A1 |
20080052711 | Forin et al. | Feb 2008 | A1 |
20080137388 | Krishnan et al. | Jun 2008 | A1 |
20080178053 | Gorman et al. | Jul 2008 | A1 |
20080215937 | Dreibelbis et al. | Sep 2008 | A1 |
20080165601 | Matick et al. | Dec 2008 | A1 |
20090067218 | Graber | Mar 2009 | A1 |
20090154238 | Lee | Jun 2009 | A1 |
20090154273 | Borot et al. | Jun 2009 | A1 |
20090254697 | Akerib | Oct 2009 | A1 |
20100064113 | Lindahl | Mar 2010 | A1 |
20100067296 | Li | Mar 2010 | A1 |
20100091582 | Vali et al. | Apr 2010 | A1 |
20100172190 | Lavi et al. | Jul 2010 | A1 |
20100210076 | Gruber et al. | Aug 2010 | A1 |
20100226183 | Kim | Sep 2010 | A1 |
20100308858 | Noda et al. | Dec 2010 | A1 |
20100332895 | Billing et al. | Dec 2010 | A1 |
20110051523 | Manabe et al. | Mar 2011 | A1 |
20110063919 | Chandrasekhar et al. | Mar 2011 | A1 |
20110093662 | Walker et al. | Apr 2011 | A1 |
20110103151 | Kim et al. | May 2011 | A1 |
20110119467 | Cadambi et al. | May 2011 | A1 |
20110122695 | Li et al. | May 2011 | A1 |
20110140741 | Zerbe et al. | Jun 2011 | A1 |
20110219260 | Nobunaga et al. | Sep 2011 | A1 |
20110267883 | Lee et al. | Nov 2011 | A1 |
20110317496 | Bunce et al. | Dec 2011 | A1 |
20120005397 | Lim et al. | Jan 2012 | A1 |
20120017039 | Margetts | Jan 2012 | A1 |
20120023281 | Kawasaki et al. | Jan 2012 | A1 |
20120106287 | Catovic | May 2012 | A1 |
20120120705 | Mitsubori et al. | May 2012 | A1 |
20120134216 | Singh | May 2012 | A1 |
20120134226 | Chow | May 2012 | A1 |
20120135225 | Chow | May 2012 | A1 |
20120140540 | Agam et al. | Jun 2012 | A1 |
20120182798 | Hosono et al. | Jul 2012 | A1 |
20120195146 | Jun et al. | Aug 2012 | A1 |
20120198310 | Tran et al. | Aug 2012 | A1 |
20120246380 | Akerib et al. | Sep 2012 | A1 |
20120265964 | Murata et al. | Oct 2012 | A1 |
20120281486 | Rao et al. | Nov 2012 | A1 |
20120303627 | Keeton et al. | Nov 2012 | A1 |
20130003467 | Klein | Jan 2013 | A1 |
20130061006 | Hein | Mar 2013 | A1 |
20130107623 | Kavalipurapu et al. | May 2013 | A1 |
20130117541 | Choquette et al. | May 2013 | A1 |
20130124783 | Yoon et al. | May 2013 | A1 |
20130132702 | Patel et al. | May 2013 | A1 |
20130138646 | Sirer et al. | May 2013 | A1 |
20130163362 | Kim | Jun 2013 | A1 |
20130173888 | Hansen et al. | Jul 2013 | A1 |
20130205114 | Badam et al. | Aug 2013 | A1 |
20130219112 | Okin et al. | Aug 2013 | A1 |
20130227361 | Bowers et al. | Aug 2013 | A1 |
20130283122 | Anholt et al. | Oct 2013 | A1 |
20130286705 | Grover et al. | Oct 2013 | A1 |
20130326154 | Haswell | Dec 2013 | A1 |
20130332707 | Gueron et al. | Dec 2013 | A1 |
20140185395 | Seo | Jul 2014 | A1 |
20140215185 | Danielsen | Jul 2014 | A1 |
20140250279 | Manning | Sep 2014 | A1 |
20140281340 | Confer | Sep 2014 | A1 |
20140281366 | Felch | Sep 2014 | A1 |
20140285662 | Murashita | Sep 2014 | A1 |
20140344934 | Jorgensen | Nov 2014 | A1 |
20150134713 | Wheeler | May 2015 | A1 |
20150143037 | Smith | May 2015 | A1 |
20150193431 | Stoytchev | Jul 2015 | A1 |
20150324290 | Leidel | Nov 2015 | A1 |
20150325272 | Murphy | Nov 2015 | A1 |
Number | Date | Country |
---|---|---|
102141905 | Aug 2011 | CN |
0214718 | Mar 1987 | EP |
2026209 | Feb 2009 | EP |
H0831168 | Feb 1996 | JP |
2009259193 | Mar 2015 | JP |
10-0211482 | Aug 1998 | KR |
10-2010-0134235 | Dec 2010 | KR |
10-2013-0049421 | May 2013 | KR |
2001065359 | Sep 2001 | WO |
2010079451 | Jul 2010 | WO |
2013062596 | May 2013 | WO |
2013081588 | Jun 2013 | WO |
2013095592 | Jun 2013 | WO |
Entry |
---|
Boyd et al., “On the General Applicability of Instruction-Set Randomization”, Jul.-Sep. 2010, (14 pgs.), vol. 7, Issue 3, IEEE Transactions on Dependable and Secure Computing. |
Stojmenovic, “Multiplicative Circulant Networks Topological Properties and Communication Algorithms”, (25 pgs.), Discrete Applied Mathematics 77 (1997) 281-305. |
“4.9.3 MINLOC and MAXLOC”, Jun. 12, 1995, (5pgs.), Message Passing Interface Forum 1.1, retrieved from http://www.mpi-forum.org/docs/mpi-1.1/mpi-11-html/node79.html. |
Derby, et al., “A High-Performance Embedded DSP Core with Novel SIMD Features”, Apr. 6-10, 2003, (4 pgs), vol. 2, pp. 301-304, 2003 IEEE International Conference on Accoustics, Speech, and Signal Processing. |
Debnath, Biplob, Bloomflash: Bloom Filter on Flash-Based Storage, 2011 31st Annual Conference on Distributed computing Systems, Jun. 20-24, 2011, 10 pgs. |
Pagiamtzis, Kostas, “Content-Addressable Memory Introduction”, Jun. 25, 2007, (6 pgs.), retrieved from: http://www.pagiamtzis.com/cam/camintro. |
Pagiamtzis, et al., “Content-Addressable Memory (CAM) Circuits and Architectures: A Tutorial and Survey”, Mar. 2006, (16 pgs.), vol. 41, No. 3, IEEE Journal of Solid-State Circuits. |
International Search Report and Written Opinion for PCT Application No. PCT/US2013/043702, dated Sep. 26, 2013, (11 pgs.). |
Elliot, et al., “Computational RAM: Implementing Processors in Memory”, Jan.-Mar. 1999, (10 pgs.), vol. 16, Issue 1, IEEE Design and Test of Computers Magazine. |
Dybdahl, et al., “Destructive-Read in Embedded DRAM, Impact on Power Consumption,” Apr. 2006, (10 pgs.), vol. 2, Issue 2, Journal of Embedded Computing-Issues in embedded single-chip multicore architectures. |
Kogge, et al., “Processing in Memory: Chips to Petaflops,” May 23, 1997, (8 pgs.), retrieved from: http://www.cs.ucf.edu/courses/cda5106/surnmer02/papers/kogge97PIM.pdf. |
Draper, et al., “The Architecture of the DIVA Processing-In-Memory Chip,” Jun. 22-26, 2002, (12 pgs.), ICS '02, retrieved from: http://www.isi.edu/˜draper/papers/ics02.pdf. |
Adibi, et al., “Processing-In-Memory Technology for Knowledge Discovery Algorithms,” Jun. 25, 2006, (10 pgs.), Proceeding of the Second International Workshop on Data Management on New Hardware, retrieved from: http://www.cs.cmu.edu/˜damon2006/pdf/adibi06inmemory.pdf. |
U.S. Appl. No. 13/449,082, entitled, “Methods and Apparatus for Pattern Matching,” filed Apr. 17, 2012, (37 pgs.). |
U.S. Appl. No. 13/743,686, entitled, “Weighted Search and Compare in a Memory Device,” filed Jan. 17, 2013, (25 pgs). |
U.S. Appl. No. 13/744,636, entitled, “Memory as a Programmable Logic Device,” filed Feb. 22, 2013, (30 pgs). |
U.S. Appl. No. 13/774,553, entitled, “Neural Network in a Memory Device,” filed Feb. 22, 2013, (63 pgs). |
U.S. Appl. No. 13/796,189, entitled, “Performing Complex Arithmetic Functions in a Memory Device,” filed Mar. 12, 2013, (23 pgs). |
Number | Date | Country | |
---|---|---|---|
20160098209 A1 | Apr 2016 | US |
Number | Date | Country | |
---|---|---|---|
62059488 | Oct 2014 | US |