A Conv‐GEMM reconfigurable accelerator with WS‐RS dataflow for high throughput processing
Feihu Wang,
Chi Zhang,
Yongchao Deng
et al.
Abstract:Convolution and matrix operations are both important computations in Deep Neural Networks (DNNs). However, the significant differences between convolution and matrix computation patterns have posed a challenge in efficiently supporting both convolution (Conv) and general matrix multiplication (GEMM) on hardware design. This paper proposes a Conv‐GEMM reconfigurable accelerator architecture for high throughput edge processing. A weight stationary‐row streaming (WS‐RS) dataflow scheme is proposed, which maximize… Show more
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