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公开(公告)号:US20180189641A1
公开(公告)日:2018-07-05
申请号:US15423279
申请日:2017-02-02
Inventor: Thomas BOESCH , Giuseppe DESOLI
Abstract: Embodiments are directed towards a hardware accelerator engine that supports efficient mapping of convolutional stages of deep neural network algorithms. The hardware accelerator engine includes a plurality of convolution accelerators, and each one of the plurality of convolution accelerators includes a kernel buffer, a feature line buffer, and a plurality of multiply-accumulate (MAC) units. The MAC units are arranged to multiply and accumulate data received from both the kernel buffer and the feature line buffer. The hardware accelerator engine also includes at least one input bus coupled to an output bus port of a stream switch, at least one output bus coupled to an input bus port of the stream switch, or at least one input bus and at least one output bus hard wired to respective output bus and input bus ports of the stream switch.
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公开(公告)号:US20180189229A1
公开(公告)日:2018-07-05
申请号:US15423272
申请日:2017-02-02
Inventor: Giuseppe DESOLI , Thomas BOESCH , Nitin CHAWLA , Surinder Pal SINGH , Elio GUIDETTI , Fabio Giuseppe DE AMBROGGI , Tommaso MAJO , Paolo Sergio ZAMBOTTI
Abstract: Embodiments are directed towards a system on chip (SoC) that implements a deep convolutional network heterogeneous architecture. The SoC includes a system bus, a plurality of addressable memory arrays coupled to the system bus, at least one applications processor core coupled to the system bus, and a configurable accelerator framework coupled to the system bus. The configurable accelerator framework is an image and deep convolutional neural network (DCNN) co-processing system. The SoC also includes a plurality of digital signal processors (DSPs) coupled to the system bus, wherein the plurality of DSPs coordinate functionality with the configurable accelerator framework to execute the DCNN.
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