Exploiting FPGAs and Spiking Neural Networks at the Micro-Edge: The EdgeAI Approach
摘要
This paper outlines the initial FPGA-centric endeavors within the EdgeAI project, targeting scenarios where extremely constrained power-energy parameters intersect with the demand for high performance and accuracy in executing Artificial Intelligence (AI) algorithms. Our discussion, after presenting the generalities of the EdgeAI project, revolves around the project objective of leveraging simultaneously event-based spiking neural networks and low-end FPGA chips for very-low-power near-sensor AI inference. We present the hardware/software implementation of this approach and the early results on the project use cases.