实现带突触延迟的脉冲神经网络在Loihi 2上的完整部署，显著提升能效与速度。
A Complete Pipeline for deploying SNNs with Synaptic Delays on Loihi 2
- 基于事件驱动训练，在GPU上优化含突触延迟的脉冲神经网络。
- 在Loihi 2上实现18倍加速、250倍节能，精度几乎无损失。
- 适合边缘计算中对低功耗高实时性有要求的应用场景。
脉冲神经网络（SNN）因其更高的能效，正成为边缘计算中替代传统人工神经网络的有力候选。类脑计算可显著降低能耗。本文提出一个完整流程：在GPU上高效进行带突触延迟的事件驱动训练，并部署至英特尔Loihi 2类脑芯片。我们在Spiking Heidelberg Digits和Spiking Speech Commands数据集上评估关键词识别任务，结果表明，引入突触延迟的模型相比无延迟架构具有更高分类准确率。基准测试显示，GPU与Loihi 2实现之间几乎无精度损失，而Loihi 2的分类速度比NVIDIA Jetson Orin Nano快18倍，能耗降低250倍。
原文摘要 · Abstract (English)
Spiking Neural Networks are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks for edge computing. Neuromorphic computing can significantly reduce energy requirements. Here, we present a complete pipeline: efficient event-based training of SNNs with synaptic delays on GPUs and deployment on Intel's Loihi 2 neuromorphic chip. We evaluate our approach on keyword recognition tasks using the Spiking Heidelberg Digits and Spiking Speech Commands datasets, demonstrating that our algorithm can enhance classification accuracy compared to architectures without delays. Our benchmarking indicates almost no accuracy loss between GPU and Loihi 2 implementations, while classification on Loihi 2 is up to 18x faster and uses 250x less energy than on an NVIDIA Jetson Orin Nano.
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