WHT demonstrates greater robustness to noise, downsampling, reduced training size, and distance metric selection, maintaining standard deviations typically below 5%, while CHT and PCA reach up to 10% under high-noise conditions.
Abstract
Implantable neural interfaces require low-power real-time signal processing to remain within strict thermal and bandwidth constraints, motivating lightweight feature extraction methods for on-chip spike sorting. This work presents the Walsh-Hadamard Transform (WHT) as a hardware-efficient feature extraction method for neural spike classification. WHT can be implemented using only adders, subtractors, and registers without coefficient memory. WHT performance is compared against the Compressed Hadamard Transform (CHT) and Principal Component Analysis (PCA), improving mean F1-scores from 55-60% to 70-75% on difficult high-noise datasets and from 90-95% to 95-99% on all other simulated datasets. In addition to improved classification performance, WHT demonstrates greater robustness to noise, downsampling, reduced training size, and distance metric selection, maintaining standard deviations typically below 5%, while CHT and PCA reach up to 10% under high-noise conditions.
This work introduces the first end-to-end neuromorphic spike-encoding and evaluation of the TIMIT dataset and quantifies the pipeline's efficiency with hardware-agnostic metrics based on the quantitative spiking activity.
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Spike sorting is an algorithmic process that extracts the activity of individual neurons from extracellular electrophysiology recordings. With the ballooning use of high-density probes, such as Neuropixels, this essential processing step is increasingly becoming time-consuming and computationally expensive. Although ma...
Samuel Garcia, Chris Halcrow, C. Windolf et al.· eLife· 0 citations
This work proposes two simplified attention mechanisms, Simplified Spiking Attention (SSA) and Ultra-Simplified Spiking Attention (USSA), which replace matrix multiplication with Hadamard products and eliminate hardware-unfriendly components such as multi-head attention and scaling.
Ting-Ting Jiang, Jiangrong Shen, Long Chen et al.· 1 citation· ⚡1
While Spiking Neural Networks (SNNs) offer a promising path toward energy-efficient edge intelligence, conventional Binary representations often suffer from high inference latency and discretization errors. Multi-level (graded) spike models address these limitations by increasing information density per pulse, yet they...
Wen-Fei Song, Andrea Castagnetti, Pierre-Emmanuel Novac et al.· Neuromorphic Computing and E...· 0 citations
The Temporal Hadamard Transformer is proposed, a spiking model co-designed with hardware for in-memory edge vision that integrates sparse algorithms with parallel hardware execution, providing a practical path toward real-time edge systems under realistic resource constraints and physical device non-idealities.
Yi-Xing Li, Wen-Hua Hu, Hao-Hui Peng et al.· Proceedings of the Internati...· 0 citations
Edge devices require short response time and low active and idle power, yet conventional neural-network hardware repeatedly moves weights and activations between memory and processing units. Spiking neural network (SNN) chips address this problem with event-driven communication, persistent neuron state, and local synap...
Le-Yang Qin· Applied and Computational En...· 0 citations
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