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

• URL: http://arxiv.org/abs/2404.18066v1
• Date: Sun, 28 Apr 2024 04:32:44 GMT
• Title: Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm
• Authors: Sai Sukruth Bezugam, Yihao Wu, JaeBum Yoo, Dmitri Strukov, Bongjin Kim,
• Abstract summary: In this study, we propose the first hardware implementation of a context-based recurrent spiking neural network (RSNN) We present a quantized version of the CLIF neuron (qCLIF), developed through a hardware-software codesign approach utilizing the sparse activity of RSNN. Our analysis spans a network configuration from 10 to 200 qCLIF neurons, supporting up to 82k synapses within a 1.86 mm2 footprint, demonstrating scalability and efficiency.
• Score: 0.3332435791857516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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