Energy-Efficient Spiking Recurrent Neural Network for Gesture Recognition on Embedded GPUs

TL;DR

This paper demonstrates that a spiking recurrent neural network with liquid time constant neurons can achieve efficient and accurate gesture recognition on embedded GPUs, significantly reducing power consumption while maintaining high performance.

Contribution

The study introduces an energy-efficient SRNN model optimized for embedded GPUs, highlighting its superior power efficiency and real-time processing capabilities for gesture recognition tasks.

Findings

14-fold increase in power efficiency on NVIDIA Jetson Nano

Batch processing improves frame rates without sacrificing accuracy

SRNN effectively interprets temporal-spatial gesture data

Abstract

Implementing AI algorithms on event-based embedded devices enables real-time processing of data, minimizes latency, and enhances power efficiency in edge computing. This research explores the deployment of a spiking recurrent neural network (SRNN) with liquid time constant neurons for gesture recognition. We focus on the energy efficiency and computational efficacy of NVIDIA Jetson Nano embedded GPU platforms. The embedded GPU showcases a 14-fold increase in power efficiency relative to a conventional GPU, making a compelling argument for its use in energy-constrained applications. The study's empirical findings also highlight that batch processing significantly boosts frame rates across various batch sizes while maintaining accuracy levels well above the baseline. These insights validate the SRNN with liquid time constant neurons as a robust model for interpreting temporal-spatial data in gesture recognition, striking a critical balance between processing speed and power frugality.