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- W3205657855 abstract "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." @default.
- W3205657855 created "2021-10-25" @default.
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- W3205657855 date "2021-08-01" @default.
- W3205657855 modified "2023-10-02" @default.
- W3205657855 title "DeepFire: Acceleration of Convolutional Spiking Neural Network on Modern Field Programmable Gate Arrays" @default.
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- W3205657855 doi "https://doi.org/10.1109/fpl53798.2021.00013" @default.
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