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

arXiv: https://arxiv.org/abs/2404.18066 · HTML: https://mlsanity.com/paper/2404.18066/

## 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.

## Builds on (most cited first, 9 of 9)

- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Long short-term memory and learning-to-learn in networks of spiking neurons](https://mlsanity.com/paper/1803.09574/) (2018)
- [The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks](https://mlsanity.com/paper/1910.07407/) (2019)
- [Online Training Through Time for Spiking Neural Networks](https://mlsanity.com/paper/2210.04195/) (2022)
- [Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences](https://mlsanity.com/paper/2005.02183/) (2020)
- [Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time](https://mlsanity.com/paper/2112.11231/) (2021)
- [Efficient recurrent architectures through activity sparsity and sparse back-propagation through time](https://mlsanity.com/paper/2206.06178/) (2022)
- [Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction](https://mlsanity.com/paper/2003.12346/) (2020)
- [sBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network with On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edge](https://mlsanity.com/paper/2002.11163/) (2020)

## 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