Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance

Qinyu Chen*, Chang Gao, Xinyuan Fang, Haitao Luan

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)

Abstract

Spiking neural networks (SNNs) are developed as a promising alternative to artificial neural networks (ANNs) due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus, they are useful to enable energy-efficient hardware inference. However, exploiting spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. In this work, we propose an FPGA-based convolutional SNN accelerator called Skydiver that exploits spatio-temporal workload balance. We propose the approximate proportional relation construction (APRC) method that can predict the relative workload channel-wisely and a channel-balanced workload schedule (CBWS) method to increase the hardware workload balance ratio to over 90%. Skydiver was implemented on a Xilinx XC7Z045 FPGA and verified on image segmentation and MNIST classification tasks. Results show improved throughput by 1.4× and 1.2× for the two tasks. Skydiver achieved 22.6KFPS throughput, and 42.4∼μ J/image prediction energy on the classification task with 98.5% accuracy.

Original languageEnglish
Pages (from-to)5732-5736
Number of pages5
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Volume41
Issue number12
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • FPGA
  • spiking neural network (SNN)
  • workload balance

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