Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm
<|reference_start|>Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm: 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<|reference_end|>
@article{bezugam2024quantized,
    title={Quantized Context Based LIF Neurons for Recurrent Spiking Neural
  Networks in 45nm},
    author={Sai Sukruth Bezugam, Yihao Wu, JaeBum Yoo, Dmitri Strukov and Bongjin
  Kim},
    journal={arXiv preprint arXiv:2404.18066},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18066},
    primaryClass={cs.NE cs.AI cs.AR cs.CV q-bio.NC}
}