Hyperdimensional Decoding of Spiking Neural Networks
This provides a compelling alternative to existing SNN decoding methods for applications requiring efficient and robust neuromorphic computing.
The paper tackles the problem of decoding spiking neural networks (SNNs) by combining them with hyperdimensional computing (HDC) to achieve high accuracy, noise robustness, low latency, and low energy usage. It results in energy consumption reductions of 1.24x to 3.67x on the DvsGesture dataset and 1.38x to 2.27x on the SL-Animals-DVS dataset, with 100% identification of unseen classes on DvsGesture.
This work presents a novel spiking neural network (SNN) decoding method, combining SNNs with Hyperdimensional computing (HDC). The goal is to create a decoding method with high accuracy, high noise robustness, low latency and low energy usage. Compared to analogous architectures decoded with existing approaches, the presented SNN-HDC model attains generally better classification accuracy, lower classification latency and lower estimated energy consumption on multiple test cases from literature. The SNN-HDC achieved estimated energy consumption reductions ranging from 1.24x to 3.67x on the DvsGesture dataset and from 1.38x to 2.27x on the SL-Animals-DVS dataset. The presented decoding method can also efficiently identify unknown classes it has not been trained on. In the DvsGesture dataset the SNN-HDC model can identify 100% of samples from an unseen/untrained class. Given the numerous benefits shown and discussed in this paper, this decoding method represents a very compelling alternative to both rate and latency decoding.