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Jun 19, 2024 · We introduce a lightweight and hardware-friendly Quantized SNN (Q-SNN) that applies quantization to both synaptic weights and membrane potentials.
Jun 19, 2024 · We introduce a lightweight and hardware-friendly Quantized SNN (Q-SNN) that applies quantization to both synaptic weights and membrane potentials.
Aug 15, 2024 · This paper proposes an efficient and effective Quantized Spiking Neural Network (Q-SNN) that compresses both weights and membrane potentials. Moreover, the ...
This repository contains the corresponding code from the paper Jason K. Eshraghian, Corey Lammie, Mostafa Rahimi Azghadi, and Wei D. Lu "Navigating Local ...
This paper introduces a lightweight spiking neural network. (SNN) architecture called Quantized SNN (Q-SNN) which prior- itizes energy efficiency while ...
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchronous event-driven manner, ...
Nov 1, 2024 · This paper introduces a lightweight spiking neural network. (SNN) architecture called Quantized SNN (Q-SNN) which prior- itizes energy ...
A prominent technique for reducing the memory footprint of Spiking Neural Networks (SNNs) without decreasing the accuracy significantly is quantization.
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We introduce a lightweight and hardware-friendly Quantized SNN (Q-SNN) that applies quantization to both synaptic weights and membrane potentials.
Oct 30, 2024 · Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) ...