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DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware

Published:28 May 2022Publication History
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Abstract

Spiking Neural Networks (SNNs) are an emerging computation model that uses event-driven activation and bio-inspired learning algorithms. SNN-based machine learning programs are typically executed on tile-based neuromorphic hardware platforms, where each tile consists of a computation unit called a crossbar, which maps neurons and synapses of the program. However, synthesizing such programs on an off-the-shelf neuromorphic hardware is challenging. This is because of the inherent resource and latency limitations of the hardware, which impact both model performance, e.g., accuracy, and hardware performance, e.g., throughput. We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware. The proposed framework works in four steps. First, it analyzes a machine learning program and generates SNN workload using representative data. Second, it partitions the SNN workload and generates clusters that fit on crossbars of the target neuromorphic hardware. Third, it exploits the rich semantics of the Synchronous Dataflow Graph (SDFG) to represent a clustered SNN program, allowing for performance analysis in terms of key hardware constraints such as number of crossbars, dimension of each crossbar, buffer space on tiles, and tile communication bandwidth. Finally, it uses a novel scheduling algorithm to execute clusters on crossbars of the hardware, guaranteeing hardware performance. We evaluate DFSynthesizer with 10 commonly used machine learning programs. Our results demonstrate that DFSynthesizer provides a much tighter performance guarantee compared to current mapping approaches.

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            • Published in

              cover image ACM Transactions on Embedded Computing Systems
              ACM Transactions on Embedded Computing Systems  Volume 21, Issue 3
              May 2022
              365 pages
              ISSN:1539-9087
              EISSN:1558-3465
              DOI:10.1145/3530307
              • Editor:
              • Tulika Mitra
              Issue’s Table of Contents

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              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 28 May 2022
              • Online AM: 26 January 2022
              • Accepted: 1 August 2021
              • Revised: 1 June 2021
              • Received: 1 November 2020
              Published in tecs Volume 21, Issue 3

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