Static Hand Gesture Recognition for American Sign Language using Neuromorphic Hardware

TL;DR

This study compares neuromorphic hardware and traditional deep neural networks for static American Sign Language gesture recognition, demonstrating high accuracy and significant power efficiency advantages of neuromorphic systems.

Contribution

The paper develops and evaluates SNN models on neuromorphic hardware for ASL recognition, providing a comprehensive comparison with DNNs on edge devices.

Findings

SNN models achieve over 99% accuracy on ASL datasets.

Neuromorphic hardware reduces power consumption by up to 20.64x.

Neuromorphic hardware reduces energy use by up to 4.10x.

Abstract

In this paper, we develop four spiking neural network (SNN) models for two static American Sign Language (ASL) hand gesture classification tasks, i.e., the ASL Alphabet and ASL Digits. The SNN models are deployed on Intel's neuromorphic platform, Loihi, and then compared against equivalent deep neural network (DNN) models deployed on an edge computing device, the Intel Neural Compute Stick 2 (NCS2). We perform a comprehensive comparison between the two systems in terms of accuracy, latency, power consumption, and energy. The best DNN model achieves an accuracy of 99.93% on the ASL Alphabet dataset, whereas the best performing SNN model has an accuracy of 99.30%. For the ASL-Digits dataset, the best DNN model achieves an accuracy of 99.76% accuracy while the SNN achieves 99.03%. Moreover, our obtained experimental results show that the Loihi neuromorphic hardware implementations achieve up to 20.64x and 4.10x reduction in power consumption and energy, respectively, when compared to NCS2.