Ultralow Power Always-On Intelligent and Connected SNN-Based System for Multimedia IoT-Enabled Applications

Abstract

The recent advances in artificial neural networks (ANNs) have created immense opportunities to achieve excellent results on the Internet of Things (IoT), which serve the uses of various real-time smart applications, such as in computer vision and speech recognition. However, the efficiency of ANNs comes at the expense of a huge number of computational resources. This tends to necessitate larger and wider ANNs unapplicable for embedded systems with limited hardware resources, e.g., mobile and wearable devices. To that end, biologically realistic spiking neural networks (SNNs) are first employed to build an always-on intelligent and connected integrated IoT-enabled system with ultralow power consumption, where we encode the temporal dynamic stimuli into effective, efficient, and reconstructable spike patterns to facilitate the subsequent processing. Herein, neural encoding plays a key role in faithfully describing the temporally rich patterns for downstream cognitive tasks. Therefore, a novel nonlinear piecewise latency coding approach for a fully event-driven SNN system is developed. Moreover, a surrogate postsynaptic potential kernel function is utilized to address the nondifferential nature of the spike generation scheme when using the error backpropagation learning method. The effectiveness of the proposal tandem with SNNs has been corroborated by indicative empirical results on different data sets serving cognitive tasks.