Stmicroelectronics S.r.l.

Stmicroelectronics International N.v.

Carmine Cappetta

Giuseppe Desoli

Thomas Boesch

Surinder Pal Singh

Saumya Suneja

Michele Rossi

Ugo Maria Iannuzzi

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.

Reconfigurable hardware buffer in a neural networks accelerator framework

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.

Vector quantization decoding hardware unit for real-time dynamic decompression for parameters of neural networks

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.

Convolution acceleration with embedded vector decompression

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.

Convolutional network hardware accelerator device, system and method

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 2and a reduction modulo 2&#x2212;1, where m is a positive integer. A plurality of MAC hardware accelerators perform modulo 2multiply-and-accumulate operations and modulo 2&#x2212;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.

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Battipaglia, Italy

Carmine Cappetta