Publication: Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance
Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance
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Chen, Q., Gao, C., Fang, X., & Luan, H. (2022). Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(12), 5732–5736. https://doi.org/10.1109/tcad.2022.3158834
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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 Skyd
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- Chen, Qinyu
- Gao, Chang
- Fang, Xinyuan
- Luan, Haitao
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Chen, Q., Gao, C., Fang, X., & Luan, H. (2022). Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(12), 5732–5736. https://doi.org/10.1109/tcad.2022.3158834
