Name
Abstract | ||
|---|---|---|
Spiking neural networks (SNNs) that enable low-power design on edge devices have recently attracted significant research. However, the temporal characteristic of SNNs causes high latency, high bandwidth and high energy consumption for the hardware. In this work, we propose a binary weight spiking model with IF-based Batch Normalization for small time steps and low hardware cost when direct trainin... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCAS51556.2021.9401181 | 2021 IEEE International Symposium on Circuits and Systems (ISCAS) |
Keywords | DocType | ISSN |
Training,Energy consumption,Neural networks,Memory management,Bandwidth,Hardware,Encoding | Conference | 0271-4302 |
ISBN | Citations | PageRank |
978-1-7281-9201-7 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong-Han Lien | 1 | 1 | 0.36 |
| Chung-Wei Hsu | 2 | 7 | 1.39 |
| Tian-Sheuan Chang | 3 | 712 | 69.10 |