Hyperdimensional Decoding of Spiking Neural Networks

📝 Original Info

• Title: Hyperdimensional Decoding of Spiking Neural Networks
• ArXiv ID: 2511.08558
• Date: 2025-11-11
• Authors: ** 제공되지 않음 (논문에 명시된 저자 정보가 없으므로 확인 필요) **

📝 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.

💡 Deep Analysis

📄 Full Content

Reference