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

Rachmad Vidya Wicaksana Putra, Muhammad Shafique

Research output: Contribution to journalArticlepeer-review


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 languageEnglish (US)
Article number9211568
Pages (from-to)3601-3613
Number of pages13
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number11
StatePublished - Nov 2020


  • Adaptivity
  • 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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