📝 SummarXiv

Abstract

Wearable devices are revolutionizing personal technology, but their usability is often hindered by frequent charging due to high power consumption. This paper introduces Distributed Neural Networks (DistNN), a framework that distributes neural network computations between resource-constrained wearable nodes and resource-rich hubs to reduce energy at the node without sacrificing performance. We define a Figure of Merit (FoM) to select the optimal split point that minimizes node-side energy. A custom hardware design using low-precision fixed-point arithmetic achieves ultra-low power while maintaining accuracy. The proposed system is ~1000x more energy efficient than a GPU and averages 11x lower power than recent machine learning (ML) ASICs at 30 fps. Evaluated with CNNs and autoencoders, DistNN attains an SSIM of 0.90 for image reconstruction and 0.89 for denoising, enabling scalable, energy-efficient, real-time wearable applications.