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

by   Twisha Titirsha, et al.

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.



There are no comments yet.


page 4


Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware

Hardware implementation of neuromorphic computing can significantly impr...

Compiling Spiking Neural Networks to Neuromorphic Hardware

Machine learning applications that are implemented with spike-based comp...

Computing with hardware neurons: spiking or classical? Perspectives of applied Spiking Neural Networks from the hardware side

While classical neural networks take a position of a leading method in t...

Principles of Stochastic Computing: Fundamental Concepts and Applications

The semiconductor and IC industry is facing the issue of high energy con...

Low-Power Neuromorphic Hardware for Signal Processing Applications

Machine learning has emerged as the dominant tool for implementing compl...

The Discrete Langevin Machine: Bridging the Gap Between Thermodynamic and Neuromorphic Systems

A formulation of Langevin dynamics for discrete systems is derived as a ...

Energy-efficient neuromorphic classifiers

Neuromorphic engineering combines the architectural and computational pr...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.