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Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm

arXiv 2404.18066 ↗First posted 28 Apr 2024cs.NE cs.AI cs.ARcited by 0

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Abstract (arXiv)

In this study, we propose the first hardware implementation of a context-based recurrent spiking neural network (RSNN) emphasizing on integrating dual information streams within the neocortical pyramidal neurons specifically Context- Dependent Leaky Integrate and Fire (CLIF) neuron models, essential element in RSNN. We present a quantized version of the CLIF neuron (qCLIF), developed through a hardware-software codesign approach utilizing the sparse activity of RSNN. Implemented in a 45nm technology node, the qCLIF is compact (900um^2) and achieves a high accuracy of 90% despite 8 bit quantization on DVS gesture classification dataset. Our analysis spans a network configuration from 10 to 200 qCLIF neurons, supporting up to 82k synapses within a 1.86 mm^2 footprint, demonstrating scalability and efficiency

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 28 Apr 2024.

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References · 9

  1. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 266
  2. 2018Long short-term memory and learning-to-learn in networks of spiking neuronscited by 181
  3. 2019The Heidelberg spiking datasets for the systematic evaluation of spiking neural networkscited by 137
  4. 2022Online Training Through Time for Spiking Neural Networkscited by 49
  5. 2020Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differencescited by 40
  6. 2021Accurate online training of dynamical spiking neural networks through Forward Propagation Through Timecited by 34
  7. 2022Efficient recurrent architectures through activity sparsity and sparse back-propagation through timecited by 26
  8. 2020Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extractioncited by 23
  9. 2020sBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network with On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edgecited by 4

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  1. 2023The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks
  2. 2025Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation
  3. 2024A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture
  4. 2021Q-SpiNN: A Framework for Quantizing Spiking Neural Networks
  5. 2025Memory-Free and Parallel Computation for Quantized Spiking Neural Networks
  6. 2024Q-SNNs: Quantized Spiking Neural Networks
  7. 2024Exploring Extreme Quantization in Spiking Language Models
  8. 2026PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks