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E-prop on SpiNNaker 2: Exploring online learning in spiking RNNs on neuromorphic hardware.

Amirhossein Rostami¹, Bernhard Vogginger¹, Yexin Yan¹

¹Chair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany.

Frontiers in Neuroscience

|December 15, 2022

Summary

This study demonstrates efficient, low-memory training of Spiking Recurrent Neural Networks (SRNNs) at the edge using the E-prop algorithm on SpiNNaker 2. This approach significantly reduces energy consumption compared to GPUs for real-time keyword spotting.

Keywords:

E-prop SpiNNaker 2 memory footprint neuromorphic hardware online learning parallelism training at the edge

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Area of Science:

Neuromorphic computing
Edge artificial intelligence (AI)
Deep learning

Background:

Deep learning models are increasingly deployed at the edge, necessitating efficient training methods.
Traditional training of recurrent neural networks (RNNs) requires significant memory for Back Propagation Through Time (BPTT).
Spiking Recurrent Neural Networks (SRNNs) offer a biologically inspired alternative, with E-prop addressing BPTT's memory limitations.

Purpose of the Study:

To implement and evaluate the E-prop algorithm for training SRNNs on the SpiNNaker 2 neuromorphic system.
To develop a parallelization strategy for efficient memory and compute utilization on SpiNNaker 2.
To assess the feasibility of real-time, on-device training for applications like keyword spotting.

Main Methods:

Implementation of the E-prop algorithm on the SpiNNaker 2 prototype.
Development of a parallelization strategy to distribute network training across SpiNNaker 2's ARM cores.
Training of an SRNN from scratch on the Google Speech Command dataset for keyword spotting.

Main Results:

Achieved 91.12% accuracy in keyword spotting with only 680 KB of memory for training.
Demonstrated significant memory efficiency compared to other spiking neural networks.
Profiling indicated E-prop's suitability for edge training and potential for architectural optimization on SpiNNaker 2.

Conclusions:

The E-prop algorithm enables efficient, low-memory SRNN training on neuromorphic hardware like SpiNNaker 2.
This approach is highly competitive for edge AI applications, offering substantial energy savings over GPUs.
Further optimization of SpiNNaker 2 architecture can accelerate online learning at the edge.