On the Role of System Software in Energy Management of Neuromorphic Computing

TL;DR

This paper investigates how system software influences energy efficiency in neuromorphic computing, proposing a heuristic mapping method that significantly reduces energy consumption across multiple applications.

Contribution

It formulates energy consumption in neuromorphic hardware and introduces a heuristic mapping approach to optimize energy efficiency.

Findings

Heuristic mapping reduces energy use in neuromorphic systems

Evaluation on 10 applications shows significant energy savings

Formulation of energy consumption considering neurons, synapses, and communication

Abstract

Neuromorphic computing systems such as DYNAPs and Loihi have recently been introduced to the computing community to improve performance and energy efficiency of machine learning programs, especially those that are implemented using Spiking Neural Network (SNN). The role of a system software for neuromorphic systems is to cluster a large machine learning model (e.g., with many neurons and synapses) and map these clusters to the computing resources of the hardware. In this work, we formulate the energy consumption of a neuromorphic hardware, considering the power consumed by neurons and synapses, and the energy consumed in communicating spikes on the interconnect. Based on such formulation, we first evaluate the role of a system software in managing the energy consumption of neuromorphic systems. Next, we formulate a simple heuristic-based mapping approach to place the neurons and synapses onto the computing resources to reduce energy consumption. We evaluate our approach with 10 machine learning applications and demonstrate that the proposed mapping approach leads to a significant reduction of energy consumption of neuromorphic computing systems.