19 citations · 42 across the 5 of their papers we have counts for
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cs.DC2021★ 5 cited
Characterizing and Demystifying the Implicit Convolution Algorithm on Commercial Matrix-Multiplication Accelerators
Yangjie Zhou, Mengtian Yang, Cong Guo +5
Many of today's deep neural network accelerators, e.g., Google's TPU and NVIDIA's tensor core, are built around accelerating the general matrix multiplication (i.e., GEMM). However…
cs.DC2020★ 13 cited
Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity
Cong Guo, Bo Yang Hsueh, Jingwen Leng +7
Network pruning can reduce the high computation cost of deep neural network (DNN) models. However, to maintain their accuracies, sparse models often carry randomly-distributed weig…
cs.DC2020
Balancing Efficiency and Flexibility for DNN Acceleration via Temporal GPU-Systolic Array Integration
Cong Guo, Yangjie Zhou, Jingwen Leng +6
The research interest in specialized hardware accelerators for deep neural networks (DNN) spikes recently owing to their superior performance and efficiency. However, today's DNN a…