Multiply-and-Fire (MNF): An Event-driven Sparse Neural Network Accelerator

• URL: http://arxiv.org/abs/2204.09797v1
• Date: Wed, 20 Apr 2022 21:56:50 GMT
• Title: Multiply-and-Fire (MNF): An Event-driven Sparse Neural Network Accelerator
• Authors: Miao Yu, Tingting Xiang, Venkata Pavan Kumar Miriyala, Trevor E. Carlson
• Abstract summary: This work takes a unique look at sparsity with an event (or activation-driven) approach to ANN acceleration. Our analytical and experimental results show that this event-driven solution presents a new direction to enable highly efficient AI inference for both CNN and workloads.
• Score: 3.224364382976958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning, particularly deep neural network inference, has become a vital workload for many computing systems, from data centers and HPC systems to edge-based computing. As advances in sparsity have helped improve the efficiency of AI acceleration, there is a continued need for improved system efficiency for both high-performance and system-level acceleration. This work takes a unique look at sparsity with an event (or activation-driven) approach to ANN acceleration that aims to minimize useless work, improve utilization, and increase performance and energy efficiency. Our analytical and experimental results show that this event-driven solution presents a new direction to enable highly efficient AI inference for both CNN and MLP workloads. This work demonstrates state-of-the-art energy efficiency and performance centring on activation-based sparsity and a highly-parallel dataflow method that improves the overall functional unit utilization (at 30 fps). This work enhances energy efficiency over a state-of-the-art solution by 1.46$\times$. Taken together, this methodology presents a novel, new direction to achieve high-efficiency, high-performance designs for next-generation AI acceleration platforms.

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