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arXiv · 2111.11744

A Customized NoC Architecture to Enable Highly Localized Computing-On-the-Move DNN Dataflow

Kaining Zhou·Yangshuo He·Rui Xiao·Jiayi Liu·Kejie Huang
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Abstract

The ever-increasing computation complexity of fastgrowing Deep Neural Networks (DNNs) has requested new computing paradigms to overcome the memory wall in conventional Von Neumann computing architectures. The emerging Computing-In-Memory (CIM) architecture has been a promising candidate to accelerate neural network computing. However, data movement between CIM arrays may still dominate the total power consumption in conventional designs. This paper proposes a flexible CIM processor architecture named Domino and "Computing-On-the-Move" (COM) dataflow, to enable stream computing and local data access to significantly reduce data movement energy. Meanwhile, Domino employs customized distributed instruction scheduling within Network-on-Chip (NoC) to implement inter-memory computing and attain mapping flexibility. The evaluation with prevailing DNN models shows that Domino achieves 1.77-to-2.37$\times$ power efficiency over several state-of-the-art CIM accelerators and improves the throughput by 1.28-to-13.16$\times$.

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Kaining Zhou, Yangshuo He, Rui Xiao, Jiayi Liu, Kejie Huang. 2021-11-23. A Customized NoC Architecture to Enable Highly Localized Computing-On-the-Move DNN Dataflow. https://arxiv.org/abs/2111.11744

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