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

TL;DR

Skydiver is an FPGA-based SNN accelerator that balances spatio-temporal workloads, significantly improving throughput and energy efficiency for image segmentation and classification tasks.

Contribution

It introduces the APRC and CBWS methods to predict and balance workloads, enhancing hardware utilization and energy efficiency in SNN accelerators.

Findings

Achieved over 90% workload balance ratio.

Improved throughput by up to 1.4X.

Reduced energy consumption to 42.4 uJ/Image.

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 1.4X and 1.2X for the two tasks. Skydiver achieved 22.6 KFPS throughput, and 42.4 uJ/Image prediction energy on the classification task with 98.5% accuracy.