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

Rachmad Vidya Wicaksana Putra, Muhammad Shafique

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.5\times $ </tex-math></inline-formula> memory saving, and improves the energy efficiency by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5\times $ </tex-math></inline-formula> on average for training and by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula> 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.