Efficient mapping of large scale SNN and rate-based DNN on SENeCA

Abstract

Artificial intelligence, machine learning, and deep learning have been the buzzwords in almost every industry (medical, automotive, defense, security, finance, etc.) for the last decade. As the market moves towards AI-based solutions, so does the computation need for these solutions increase and change with time. With the rise of smart cities and cyberphysical systems, the need for edge devices and efficient computation on the edge increases. While most of these newly developed deep learning models are quite large and wasteful in terms of energy, there have been recent methods that help improve the performance on the edge. However, due to their size, variety, and irregularity, the computing and power requirements are often too large to deploy these models on edge devices. This prohibits the application of such models within a rich field of application that requires high-throughput and real-time execution.

SENeCA (Scalable Energy Efficient Neuromorphic Computing Architecture) is a next-generation RISC-V-based neuromorphic computing architecture that was designed primarily for ultralow-edge applications where adaptivity is required. To mathematically model SENeCA, SENSIM (Scalable Energy Efficient Simulator, an open source simulator developed by the Interuniversity Microelectronic Center) provides an accurate mathematical software model of SENeCA, which helps in the early development and realization of a spiking neural network and deep neural network. This thesis work develops an efficient mapping tool SENMap (Scalable Energy-Efficient Neuromorphic Computing Architecture Mapper) on top of SENSIM which maps spiking neural networks efficiently. Having a faster, scalable realization software solution that can cater to large-scale neural networks can speed up the development procedure.

SENMap is developed in such a way that it supports flexible SNN/DNN application replacement, multiple single- and multi-objective optimization algorithms; the flexibility to choose from different optimization strategies; and also varying architectural parameters at the time of experimentation. Results show that mapping and neural processing elements (NPEs) depend primarily on the rate at which the sensor processes the data. On the basis of the rate, an early realization of SNN- and DNN-based edge AI chips SENMap. Depending on the actual parameters used, the maximum achieved improvements in energy consumption was around ~40%.