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A Novel CONV Acceleration Strategy Based on Logical PE Set Segmentation for Row Stationary Dataflow | IEEE Journals & Magazine | IEEE Xplore

A Novel CONV Acceleration Strategy Based on Logical PE Set Segmentation for Row Stationary Dataflow


Abstract:

Deep convolutional neural networks (DCNNs) have been proposed as enhanced developments of neural networks (NNs) in the field of artificial intelligence (AI) and successfu...Show More

Abstract:

Deep convolutional neural networks (DCNNs) have been proposed as enhanced developments of neural networks (NNs) in the field of artificial intelligence (AI) and successfully applied in deep learning (DL) scenarios. With the advancement of technology, the number of network layers has continuously increased, resulting in a huge number of calculations and memory accesses required in the training and inference process of DCNNs and thereby hindering their further deployment and application. Using a specific dataflow formed by reusable DCNN data in the network-on-chip (NoC), reducing the memory access pressure and improving DCNN processing efficiency has become a promising acceleration scheme for the current DCNN. In this article, a novel convolution layer (CONV) acceleration strategy based on logical PE set segmentation for row stationary (RS) dataflow is proposed to solve the problems of low flexibility and inefficient processing array utilization faced by the conventional folding mapping strategy. The simulation results show that the new mapping strategy based on PE set segmentation can achieve better processing element utilization and CONV acceleration improvement at the expense of little increase in the data movement energy consumption compared with the conventional strategy.
Published in: IEEE Transactions on Computers ( Volume: 71, Issue: 6, 01 June 2022)
Page(s): 1466 - 1478
Date of Publication: 15 June 2021

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