Hyperdimensional Decoding of Spiking Neural Networks

• URL: http://arxiv.org/abs/2511.08558v2
• Date: Thu, 13 Nov 2025 01:45:24 GMT
• Title: Hyperdimensional Decoding of Spiking Neural Networks
• Authors: Cedrick Kinavuidi, Luca Peres, Oliver Rhodes,
• Abstract summary: This work presents a novel spiking neural network (SNN) decoding method, combining SNNs with Hyperdimensional computing (HDC)<n>The goal is to create a decoding method with high accuracy, high noise robustness, low latency and low energy usage.
• Score: 0.17646262965516948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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