Abstract: A device includes a plurality of hardware accelerator islands. The accelerator islands have a plurality of processing elements, a plurality of streaming engines, and a stream switch coupled to the plurality of processing elements and to the plurality of streaming engines. The stream switch streams data between the plurality of processing elements of the accelerator island, and between the plurality of streaming engines of the accelerator island and the plurality of processing elements of the accelerator island. Unidirectional stream switch connections (SSCONNs) are coupled between pairs of stream switches of the plurality of accelerator islands. The stream switches of the plurality of hardware accelerator islands and the SSCONNs form a run-time reconfigurable interconnection mesh between the plurality of processing elements of the plurality of hardware accelerator islands.

Abstract: A neural network is able to reconfigure hardware accelerators on-the-fly without stopping downstream hardware accelerators. The neural network inserts a reconfiguration tag into the stream of feature data. If the reconfiguration tag matches an identification of a hardware accelerator, a reconfiguration process is initiated. Upstream hardware accelerators are paused while downstream hardware accelerators continue to operate. An epoch controller reconfigures the hardware accelerator via a bus. Normal operation of the neural network then resumes.

Abstract: A device includes a plurality of hardware accelerator islands. The accelerator islands have a plurality of processing elements, a plurality of streaming engines, and a stream switch coupled to the plurality of processing elements and to the plurality of streaming engines. The stream switch streams data between the plurality of processing elements of the accelerator island, and between the plurality of streaming engines of the accelerator island and the plurality of processing elements of the accelerator island. Unidirectional stream switch connections (SSCONNs) are coupled between pairs of stream switches of the plurality of accelerator islands. The stream switches of the plurality of hardware accelerator islands and the SSCONNs form a run-time reconfigurable interconnection mesh between the plurality of processing elements of the plurality of hardware accelerator islands.

Abstract: A hardware accelerator includes a plurality of functional circuits, a stream switch, one or more data reshape units coupled to the plurality of functional circuits via the stream switch to stream data to and from functional circuits of the plurality of functional circuits, and one or more In-Memory Computing (IMC) clusters coupled to the stream switch. In operation, inactive IMC devices of at least a subset of the one or more IMC clusters are accessible to at least a subset of the one or more data reshape units, via memory interface independent from the stream switch, to serve as at least part of Tightly-Coupled Memory (TCM) dedicated to at least one of the one or more data reshape units.

Abstract: A device includes a plurality of hardware accelerator islands. The accelerator islands have a plurality of processing elements, a plurality of streaming engines, and a stream switch coupled to the plurality of processing elements and to the plurality of streaming engines. The stream switch streams data between the plurality of processing elements of the accelerator island, and between the plurality of streaming engines of the accelerator island and the plurality of processing elements of the accelerator island. Unidirectional stream switch connections (SSCONNs) are coupled between pairs of stream switches of the plurality of accelerator islands. The stream switches of the plurality of hardware accelerator islands and the SSCONNs form a run-time reconfigurable interconnection mesh between the plurality of processing elements of the plurality of hardware accelerator islands.

Abstract: A neural network is able to reconfigure hardware accelerators on-the-fly without stopping downstream hardware accelerators. The neural network inserts a reconfiguration tag into the stream of feature data. If the reconfiguration tag matches an identification of a hardware accelerator, a reconfiguration process is initiated. Upstream hardware accelerators are paused while downstream hardware accelerators continue to operate. An epoch controller reconfigures the hardware accelerator via a bus. Normal operation of the neural network then resumes.

Abstract: A neural network includes an internal storage unit. The internal storage unit stores feature data received from a memory external to the neural network. The internal storage unit reads the feature data to a hardware accelerator of the neural network. The internal storage unit adapts a storage pattern of the feature data and a read pattern of the feature data to enhance the efficiency of the hardware accelerator.

Abstract: A neural network includes an internal storage unit. The internal storage unit stores feature data received from a memory external to the neural network. The internal storage unit reads the feature data to a hardware accelerator of the neural network. The internal storage unit adapts a storage pattern of the feature data and a read pattern of the feature data to enhance the efficiency of the hardware accelerator.

Abstract: A convolutional accelerator framework (CAF) has a plurality of processing circuits including one or more convolution accelerators, a reconfigurable hardware buffer configurable to store data of a variable number of input data channels, and a stream switch coupled to the plurality of processing circuits. The reconfigurable hardware buffer has a memory and control circuitry. A number of the variable number of input data channels is associated with an execution epoch. The stream switch streams data of the variable number of input data channels between processing circuits of the plurality of processing circuits and the reconfigurable hardware buffer during processing of the execution epoch. The control circuitry of the reconfigurable hardware buffer configures the memory to store data of the variable number of input data channels, the configuring including allocating a portion of the memory to each of the variable number of input data channels.

Abstract: Embodiments of an electronic device include an integrated circuit, a reconfigurable stream switch formed in the integrated circuit along with a plurality of convolution accelerators and a decompression unit coupled to the reconfigurable stream switch. The decompression unit decompresses encoded kernel data in real time during operation of convolutional neural network.

Abstract: Techniques and systems are provided for implementing a convolutional neural network. One or more convolution accelerators are provided that each include a feature line buffer memory, a kernel buffer memory, and a plurality of multiply-accumulate (MAC) circuits arranged to multiply and accumulate data. In a first operational mode the convolutional accelerator stores feature data in the feature line buffer memory and stores kernel data in the kernel data buffer memory. In a second mode of operation, the convolutional accelerator stores kernel decompression tables in the feature line buffer memory.

Abstract: A Multiple Accumulate (MAC) hardware accelerator includes a plurality of multipliers. The plurality of multipliers multiply a digit-serial input having a plurality of digits by a parallel input having a plurality of bits by sequentially multiplying individual digits of the digit-serial input by the plurality of bits of the parallel input. A result is generated based on the multiplication of the digit-serial input by the parallel input. An accelerator framework may include multiple MAC hardware accelerators, and may be used to implement a convolutional neural network. The MAC hardware accelerators may multiple an input weight by an input feature by sequentially multiplying individual digits of the input weight by the input feature.

Abstract: Embodiments of an electronic device include an integrated circuit, a reconfigurable stream switch formed in the integrated circuit along with a plurality of convolution accelerators and a decompression unit coupled to the reconfigurable stream switch. The decompression unit decompresses encoded kernel data in real time during operation of convolutional neural network.

Abstract: Techniques and systems are provided for implementing a convolutional neural network. One or more convolution accelerators are provided that each include a feature line buffer memory, a kernel buffer memory, and a plurality of multiply-accumulate (MAC) circuits arranged to multiply and accumulate data. In a first operational mode the convolutional accelerator stores feature data in the feature line buffer memory and stores kernel data in the kernel data buffer memory. In a second mode of operation, the convolutional accelerator stores kernel decompression tables in the feature line buffer memory.

Abstract: Embodiments of an electronic device include an integrated circuit, a reconfigurable stream switch formed in the integrated circuit along with a plurality of convolution accelerators and a decompression unit coupled to the reconfigurable stream switch. The decompression unit decompresses encoded kernel data in real time during operation of convolutional neural network.

Abstract: Techniques and systems are provided for implementing a convolutional neural network. One or more convolution accelerators are provided that each include a feature line buffer memory, a kernel buffer memory, and a plurality of multiply-accumulate (MAC) circuits arranged to multiply and accumulate data. In a first operational mode the convolutional accelerator stores feature data in the feature line buffer memory and stores kernel data in the kernel data buffer memory. In a second mode of operation, the convolutional accelerator stores kernel decompression tables in the feature line buffer memory.

Abstract: A system includes an addressable memory array, one or more processing cores, and an accelerator framework coupled to the addressable memory. The accelerator framework includes a Multiply ACcumulate (MAC) hardware accelerator cluster. The MAC hardware accelerator cluster has a binary-to-residual converter, which, in operation, converts binary inputs to a residual number system. Converting a binary input to the residual number system includes a reduction modulo 2m and a reduction modulo 2m?1, where m is a positive integer. A plurality of MAC hardware accelerators perform modulo 2m multiply-and-accumulate operations and modulo 2m?1 multiply-and-accumulate operations using the converted binary input. A residual-to-binary converter generates a binary output based on the output of the MAC hardware accelerators.

Abstract: A convolutional accelerator framework (CAF) has a plurality of processing circuits including one or more convolution accelerators, a reconfigurable hardware buffer configurable to store data of a variable number of input data channels, and a stream switch coupled to the plurality of processing circuits. The reconfigurable hardware buffer has a memory and control circuitry. A number of the variable number of input data channels is associated with an execution epoch. The stream switch streams data of the variable number of input data channels between processing circuits of the plurality of processing circuits and the reconfigurable hardware buffer during processing of the execution epoch. The control circuitry of the reconfigurable hardware buffer configures the memory to store data of the variable number of input data channels, the configuring including allocating a portion of the memory to each of the variable number of input data channels.

Abstract: Techniques and systems are provided for implementing a convolutional neural network. One or more convolution accelerators are provided that each include a feature line buffer memory, a kernel buffer memory, and a plurality of multiply-accumulate (MAC) circuits arranged to multiply and accumulate data. In a first operational mode the convolutional accelerator stores feature data in the feature line buffer memory and stores kernel data in the kernel data buffer memory. In a second mode of operation, the convolutional accelerator stores kernel decompression tables in the feature line buffer memory.

Abstract: Embodiments of an electronic device include an integrated circuit, a reconfigurable stream switch formed in the integrated circuit along with a plurality of convolution accelerators and a decompression unit coupled to the reconfigurable stream switch. The decompression unit decompresses encoded kernel data in real time during operation of convolutional neural network.