# FSpiNN: An Optimization Framework for Memory-Efficient and Energy-Efficient Spiking Neural Networks

Research output: Contribution to journalArticlepeer-review

## Abstract

Spiking neural networks (SNNs) are gaining interest due to their event-driven processing which potentially consumes low-power/energy computations in hardware platforms while offering unsupervised learning capability due to the spike-timing-dependent plasticity (STDP) rule. However, state-of-the-art SNNs require a large memory footprint to achieve high accuracy, thereby making them difficult to be deployed on embedded systems, for instance, on battery-powered mobile devices and IoT Edge nodes. Toward this, we propose FSpiNN, an optimization framework for obtaining memory-efficient and energy-efficient SNNs for training and inference processing, with unsupervised learning capability while maintaining accuracy. It is achieved by: 1) reducing the computational requirements of neuronal and STDP operations; 2) improving the accuracy of STDP-based learning; 3) compressing the SNN through a fixed-point quantization; and 4) incorporating the memory and energy requirements in the optimization process. FSpiNN reduces the computational requirements by reducing the number of neuronal operations, the STDP-based synaptic weight updates, and the STDP complexity. To improve the accuracy of learning, FSpiNN employs timestep-based synaptic weight updates and adaptively determines the STDP potentiation factor and the effective inhibition strength. The experimental results show that as compared to the state-of-the-art work, FSpiNN achieves $7.5\times$ memory saving, and improves the energy efficiency by $3.5\times$ on average for training and by $1.8\times$ on average for inference, across MNIST and Fashion MNIST datasets, with no accuracy loss for a network with 4900 excitatory neurons, thereby enabling energy-efficient SNNs for edge devices/embedded systems.

Original language English (US) 9211568 3601-3613 13 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39 11 https://doi.org/10.1109/TCAD.2020.3013049 Published - Nov 2020

## Keywords

• edge devices
• embedded systems
• energy efficiency
• framework
• memory
• optimization
• spike-timing-dependent plasticity (STDP)
• spiking neural networks (SNNs)
• unsupervised learning

## ASJC Scopus subject areas

• Software
• Computer Graphics and Computer-Aided Design
• Electrical and Electronic Engineering

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