Energy-Efficient Spiking Recurrent Neural Network for Gesture Recognition on Embedded GPUs
This work addresses energy-efficient AI for gesture recognition on edge devices, representing an incremental improvement through optimized deployment on embedded hardware.
The researchers tackled gesture recognition by deploying a spiking recurrent neural network with liquid time constant neurons on embedded GPUs, achieving a 14-fold increase in power efficiency compared to conventional GPUs while maintaining accuracy above baseline levels.
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.