Paper Info
Title
Deep Learning Incorporating Biologically Inspired Neural Dynamics And In-Memory Computing
Abstract
Spiking neural networks (SNNs) incorporating biologically plausible neurons hold great promise because of their unique temporal dynamics and energy efficiency. However, SNNs have developed separately from artificial neural networks (ANNs), limiting the impact of deep learning advances for SNNs. Here, we present an alternative perspective of the spiking neuron that incorporates its neural dynamics into a recurrent ANN unit called a spiking neural unit (SNU). SNUs may operate as SNNs, using a step function activation, or as ANNs, using continuous activations. We demonstrate the advantages of SNU dynamics through simulations on multiple tasks and obtain accuracies comparable to, or better than, those of ANNs. The SNU concept enables an efficient implementation with in-memory acceleration for both training and inference. We experimentally demonstrate its efficacy for a music-prediction task in an in-memory-based SNN accelerator prototype using 52,800 phase-change memory devices. Our results open up an avenue for broad adoption of biologically inspired neural dynamics in challenging applications and acceleration with neuromorphic hardware.Spiking neural networks and in-memory computing are both promising routes towards energy-efficient hardware for deep learning. Wozniak et al. incorporate the biologically inspired dynamics of spiking neurons into conventional recurrent neural network units and in-memory computing, and show how this allows for accurate and energy-efficient deep learning.
Year
DOI
Venue
2020
10.1038/s42256-020-0187-0
NATURE MACHINE INTELLIGENCE
DocType
Volume
Issue
Journal
2
6
Citations 
PageRank 
References 
4
0.41
0
Authors
4
Name
Order
Citations
PageRank
Stanisław Woźniak140.41
Angeliki Pantazi27511.34
Thomas Bohnstingl340.75
Evangelos Eleftheriou41590118.20