Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors

• URL: http://arxiv.org/abs/2102.06408v1
• Date: Fri, 12 Feb 2021 09:20:49 GMT
• Title: Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
• Authors: Julian B\"uchel, Dmitrii Zendrikov, Sergio Solinas, Giacomo Indiveri, Dylan R. Muir
• Abstract summary: Mixed-signal analog/digital electronic circuits can emulate spiking neurons and synapses with extremely high energy efficiency. Mismatch is expressed as differences in effective parameters between identically-configured neurons and synapses. We present a supervised learning approach that addresses this challenge by maximizing robustness to mismatch and other common sources of noise.
• Score: 2.6949002029513167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixed-signal analog/digital electronic circuits can emulate spiking neurons and synapses with extremely high energy efficiency, following an approach known as "neuromorphic engineering". However, analog circuits are sensitive to variation in fabrication among transistors in a chip ("device mismatch"). In the case of neuromorphic implementation of Spiking Neural Networks (SNNs), mismatch is expressed as differences in effective parameters between identically-configured neurons and synapses. Each fabricated chip therefore provides a different distribution of parameters such as time constants or synaptic weights. Without the expensive overhead in terms of area and power of extra on-chip learning or calibration circuits, device mismatch and other noise sources represent a critical challenge for the deployment of pre-trained neural network chips. Here we present a supervised learning approach that addresses this challenge by maximizing robustness to mismatch and other common sources of noise. The proposed method trains (SNNs) to perform temporal classification tasks by mimicking a pre-trained dynamical system, using a local learning rule adapted from non-linear control theory. We demonstrate the functionality of our model on two tasks that require memory to perform successfully, and measure the robustness of our approach to several forms of noise and variability present in the network. We show that our approach is more robust than several common alternative approaches for training SNNs. Our method provides a viable way to robustly deploy pre-trained networks on mixed-signal neuromorphic hardware, without requiring per-device training or calibration.

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