| Autor: |
Myat Thu Linn Aung, Tao Luo, Rick Siow Mong Goh, Weng-Fai Wong, Liwei Yang, Chuping Qu |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
FPL |
| DOI: |
10.1109/fpl53798.2021.00013 |
| Popis: |
Spiking neural networks (SNN) with their ‘integrate and fire’ (I&F) neurons replace the hardware-intensive multiply-accumulate (MAC) operations in convolutional neural networks (CNN) with accumulate operations — not only making it easy to implement on FPGAs but also opening up the opportunities for energy-efficient hardware acceleration. In this paper, we propose DeepFire — the high-performance RTL IP — for accelerating convolutional SNN inference. The IP exploits various resources available on modern FPGAs, and it outperforms existing SNN implementations by more than 10× in terms of both frame per second (FPS) and performance per watt (FPS/Watt). Our design achieves up to 40.1kFPS and 28.3kFPS on MNIST and CIFAR-10/SVHN datasets with 99.14% and 81.8%/93.1% accuracies respectively. IP was evaluated with 7-series and Ultrascale+ FPGAs from Xilinx achieving Fmax of 375MHz and 500MHz respectively. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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