A Complete Pipeline for deploying SNNs with Synaptic Delays on Loihi 2
This work addresses energy efficiency for edge computing applications using neuromorphic hardware, representing an incremental advancement in SNN deployment pipelines.
The authors tackled the problem of deploying energy-efficient Spiking Neural Networks (SNNs) with synaptic delays for edge computing by developing a pipeline for training on GPUs and deployment on Intel's Loihi 2 neuromorphic chip. They demonstrated on keyword recognition tasks that their approach enhances classification accuracy compared to delay-free architectures, with Loihi 2 achieving up to 18x faster classification and 250x less energy usage than an NVIDIA Jetson Orin Nano while maintaining accuracy.
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.