A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection

Abstract

This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms. The network architecture is based on Convolutional SNN using leaky-integrate-fire neuron models. The model combines unsupervised Spike Time-Dependent Plasticity (STDP) learning with back-propagation (STBP) learning methods and also uses Monte Carlo Dropout to get an estimate of the uncertainty error. FSHNN provides better accuracy compared to DNN based object detectors while being 150X energy-efficient. It also outperforms these object detectors, when subjected to noisy input data and less labeled training data with a lower uncertainty error.

Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles

“…Spiking neural networks (SNNs) [1] use unsupervised bio-inspired neurons and synaptic connections, trainable with either biological learning rules such as spike-timingdependent plasticity (STDP) [2] or supervised statistical learning algorithms such as surrogate gradient [3]. Empirical results on standard SNNs also show good performance for various tasks, including spatiotemporal data classification, [4,5], sequence-tosequence mapping [6], object detection [7,8], and universal function approximation [9,10]. An important motivation for the application of SNN in machine learning (ML) is the sparsity in the firing (activation) of the neurons, which reduces energy dissipation during inference [11].…”