Abstract: A method for improving performance of a predefined Deep Neural Network (DNN) convolution processing on a computing device includes inputting parameters, as input data into a processor on a computer that formalizes a design space exploration of a convolution mapping, on a predefined computer architecture that will execute the predefined convolution processing. The parameters are predefined as guided by a specification for the predefined convolution processing to be implemented by the convolution mapping and by a microarchitectural specification for the processor that will execute the predefined convolution processing. The processor calculates performance metrics for executing the predefined convolution processing on the computing device, as functions of the predefined parameters, as proxy estimates of performance of different possible design choices to implement the predefined convolution processing.

Abstract: A convolutional lowering component (CoLor component) between processor and memory units (or within a memory hierarchy) maps location in a lowered matrix to an equivalent location in a non-lowered matrix and provides auto zero padding in computational heavy convolutional layers. An identification component identifies processing components that execute computations in deep neural networks (DNNs) in which convolutions are realized as general matrix to matrix multiplications (GEMM) operations, and identifies a subset of the processing components that store deep neural network (DNN) features in a non-lowered form component that determines output for successively larger neural networks of a set. An address translation component translates address requests, generated by the subset of processing components to a memory subsystem, from a lowered index form to a non-lowered index form.

Abstract: Techniques facilitating matrix multiplication on a systolic array are provided. A computer-implemented method can comprise populating, by a system operatively coupled to a processor, respective first registers of one or more processing elements of a systolic array structure with respective input data bits of a first data matrix. The one or more processing elements can comprise a first processing element that comprises a first input data bit of the first data matrix and a first activation bit of a second data matrix. The method can also include determining, by the system, at the first processing element, a first partial sum of a third data matrix. Further, the method can include streaming, by the system, the first partial sum of the third data matrix from the first processing element.

Abstract: An apparatus and method for supporting simultaneous multiple iterations (SMI) and iteration level commits (ILC) in a course grained reconfigurable architecture (CGRA). The apparatus includes: Hardware structures that connect all of multiple processing engines (PEs) to a load-store unit (LSU) configured to keep track of which compiled program code iterations have completed, which ones are in flight and which are yet to begin, and a control unit including hardware structures that are used to maintain synchronization and initiate and terminate loops within the PEs. The PEs, LSU and control unit are configured to commit instructions, and save and restore context at loop iteration boundaries. In doing so, the apparatus tracks and buffers state of in-flight iterations, and detects conditions that prevent an iteration from completing.

Abstract: A compensated deep neural network (compensated-DNN) is provided. A first vector having a set of components and a second vector having a set of corresponding components are received. A component of the first vector includes a first quantized value and a first compensation instruction, and a corresponding component of the second vector includes a second quantized value and a second compensation instruction. The first quantized value is multiplied with the second quantized value to compute a raw product value. The raw product value is compensated for a quantization error according to the first and second compensation instructions to produce a compensated product value. The compensated product value is added into an accumulated value for the dot product. The accumulated value is converted into an output vector of the dot product. The output vector includes an output quantized value and an output compensation instruction.

Abstract: Techniques facilitating matrix multiplication on a systolic array are provided. A computer-implemented method can comprise populating, by a system operatively coupled to a processor, respective first registers of one or more processing elements of a systolic array structure with respective input data bits of a first data matrix. The one or more processing elements can comprise a first processing element that comprises a first input data bit of the first data matrix and a first activation bit of a second data matrix. The method can also include determining, by the system, at the first processing element, a first partial sum of a third data matrix. Further, the method can include streaming, by the system, the first partial sum of the third data matrix from the first processing element.

Abstract: Techniques facilitating matrix multiplication on a systolic array are provided. A computer-implemented method can comprise populating, by a system operatively coupled to a processor, respective first registers of one or more processing elements of a systolic array structure with respective input data bits of a first data matrix. The one or more processing elements can comprise a first processing element that comprises a first input data bit of the first data matrix and a first activation bit of a second data matrix. The method can also include determining, by the system, at the first processing element, a first partial sum of a third data matrix. Further, the method can include streaming, by the system, the first partial sum of the third data matrix from the first processing element.

Abstract: A scheme referred to as a “Region-based cache restoration prefetcher” (RECAP) is employed for cache preloading on a partition or a context switch. The RECAP exploits spatial locality to provide a bandwidth-efficient prefetcher to reduce the “cold” cache effect caused by multiprogrammed virtualization. The RECAP groups cache blocks into coarse-grain regions of memory, and predicts which regions contain useful blocks that should be prefetched the next time the current virtual machine executes. Based on these predictions, and using a simple compression technique that also exploits spatial locality, the RECAP provides a robust prefetcher that improves performance without excessive bandwidth overhead or slowdown.

Abstract: A convolutional lowering component (CoLor component) between processor and memory units (or within a memory hierarchy) maps location in a lowered matrix to an equivalent location in a non-lowered matrix and provides auto zero padding in computational heavy convolutional layers. An identification component identifies processing components that execute computations in deep neural networks (DNNs) in which convolutions are realized as general matrix to matrix multiplications (GEMM) operations, and identifies a subset of the processing components that store deep neural network (DNN) features in a non-lowered form component that determines output for successively larger neural networks of a set. An address translation component translates address requests, generated by the subset of processing components to a memory subsystem, from a lowered index form to a non-lowered index form.

Abstract: A scheme referred to as a “Region-based cache restoration prefetcher” (RECAP) is employed for cache preloading on a partition or a context switch. The RECAP exploits spatial locality to provide a bandwidth-efficient prefetcher to reduce the “cold” cache effect caused by multiprogrammed virtualization. The RECAP groups cache blocks into coarse-grain regions of memory, and predicts which regions contain useful blocks that should be prefetched the next time the current virtual machine executes. Based on these predictions, and using a simple compression technique that also exploits spatial locality, the RECAP provides a robust prefetcher that improves performance without excessive bandwidth overhead or slowdown.

Abstract: Techniques facilitating matrix multiplication on a systolic array are provided. A computer-implemented method can comprise populating, by a system operatively coupled to a processor, respective first registers of one or more processing elements of a systolic array structure with respective input data bits of a first data matrix. The one or more processing elements can comprise a first processing element that comprises a first input data bit of the first data matrix and a first activation bit of a second data matrix. The method can also include determining, by the system, at the first processing element, a first partial sum of a third data matrix. Further, the method can include streaming, by the system, the first partial sum of the third data matrix from the first processing element.

Abstract: Techniques facilitating matrix multiplication on a systolic array are provided. A computer-implemented method can comprise populating, by a system operatively coupled to a processor, respective first registers of one or more processing elements of a systolic array structure with respective input data bits of a first data matrix. The one or more processing elements can comprise a first processing element that comprises a first input data bit of the first data matrix and a first activation bit of a second data matrix. The method can also include determining, by the system, at the first processing element, a first partial sum of a third data matrix. Further, the method can include streaming, by the system, the first partial sum of the third data matrix from the first processing element.

Abstract: A Deep Neural Networks (DNN) analysis method, system, and computer program product include characterizing a space of possible configurations for a DNN, evaluating a metric-of-interest for a configuration of the possible configurations, and searching the space to identify a configuration of the possible configurations that maximizes the metric-of-interest.

Abstract: An apparatus and method for supporting simultaneous multiple iterations (SMI) in a course grained reconfigurable architecture (CGRA). In support of SMI, the apparatus includes: Hardware structures that connect all of multiple processing engines (PEs) to a load-store unit (LSU) configured to keep track of which compiled program code iterations have completed, which ones are in flight and which are yet to begin, and a control unit including hardware structures that are used to maintain synchronization and initiate and terminate loops within the PEs. SMI permits execution of the next instruction within any iteration (in flight). If instructions from multiple iterations are ready for execution (and are pre-decoded), then the hardware selects the lowest iteration number ready for execution. If in a particular clock cycle, a loop iteration with a lower iteration number is stalled (i.e.

Abstract: In a decode stage of hardware processor pipeline, one particular instruction of a plurality of instructions is decoded. It is determined that the particular instruction requires a memory access. Responsive to such determination, it is predicted whether the memory access will result in a cache miss. The predicting in turn includes accessing one of a plurality of entries in a pattern history table stored as a hardware table in the decode stage. The accessing is based, at least in part, upon at least a most recent entry in a global history buffer. The pattern history table stores a plurality of predictions. The global history buffer stores actual results of previous memory accesses as one of cache hits and cache misses.

Abstract: Techniques facilitating matrix multiplication on a systolic array are provided. A computer-implemented method can comprise populating, by a system operatively coupled to a processor, respective first registers of one or more processing elements of a systolic array structure with respective input data bits of a first data matrix. The one or more processing elements can comprise a first processing element that comprises a first input data bit of the first data matrix and a first activation bit of a second data matrix. The method can also include determining, by the system, at the first processing element, a first partial sum of a third data matrix. Further, the method can include streaming, by the system, the first partial sum of the third data matrix from the first processing element.

Abstract: Techniques facilitating matrix multiplication on a systolic array are provided. A computer-implemented method can comprise populating, by a system operatively coupled to a processor, respective first registers of one or more processing elements of a systolic array structure with respective input data bits of a first data matrix. The one or more processing elements can comprise a first processing element that comprises a first input data bit of the first data matrix and a first activation bit of a second data matrix. The method can also include determining, by the system, at the first processing element, a first partial sum of a third data matrix. Further, the method can include streaming, by the system, the first partial sum of the third data matrix from the first processing element.

Abstract: In a decode stage of hardware processor pipeline, one particular instruction of a plurality of instructions is decoded. It is determined that the particular instruction requires a memory access. Responsive to such determination, it is predicted whether the memory access will result in a cache miss. The predicting in turn includes accessing one of a plurality of entries in a pattern history table stored as a hardware table in the decode stage. The accessing is based, at least in part, upon at least a most recent entry in a global history buffer. The pattern history table stores a plurality of predictions. The global history buffer stores actual results of previous memory accesses as one of cache hits and cache misses.

Abstract: A scheme referred to as a “Region-based cache restoration prefetcher” (RECAP) is employed for cache preloading on a partition or a context switch. The RECAP exploits spatial locality to provide a bandwidth-efficient prefetcher to reduce the “cold” cache effect caused by multiprogrammed virtualization. The RECAP groups cache blocks into coarse-grain regions of memory, and predicts which regions contain useful blocks that should be prefetched the next time the current virtual machine executes. Based on these predictions, and using a simple compression technique that also exploits spatial locality, the RECAP provides a robust prefetcher that improves performance without excessive bandwidth overhead or slowdown.

Abstract: A scheme referred to as a “Region-based cache restoration prefetcher” (RECAP) is employed for cache preloading on a partition or a context switch. The RECAP exploits spatial locality to provide a bandwidth-efficient prefetcher to reduce the “cold” cache effect caused by multiprogrammed virtualization. The RECAP groups cache blocks into coarse-grain regions of memory, and predicts which regions contain useful blocks that should be prefetched the next time the current virtual machine executes. Based on these predictions, and using a simple compression technique that also exploits spatial locality, the RECAP provides a robust prefetcher that improves performance without excessive bandwidth overhead or slowdown.