Jun 19, 2024 · We introduce a lightweight and hardware-friendly Quantized SNN (Q-SNN) that applies quantization to both synaptic weights and membrane potentials.
This repository contains the corresponding code from the paper Jason K. Eshraghian, Corey Lammie, Mostafa Rahimi Azghadi, and Wei D. Lu "Navigating Local ...
Anonymous Authors. ABSTRACT. Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchro-.
Jun 19, 2024 · We introduce a lightweight and hardware-friendly Quantized SNN (Q-SNN) that applies quantization to both synaptic weights and membrane potentials.
A prominent technique for reducing the memory footprint of Spiking Neural Networks (SNNs) without decreasing the accuracy significantly is quantization.
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Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchronous event-driven manner, ...
Jul 18, 2022 · A comprehensive quantization framework for fast SNNs (QFFS) is built including the proposed information compression, noise suppression techniques, and other ...
The Q-SpiNN is proposed, a novel quantization framework for memory-efficient SNNs that employs quantization for different SNN parameters based on their ...
Jun 23, 2024 · We introduce a lightweight and hardware-friendly Quantized SNN (Q-SNN) that applies quantization to both synaptic weights and membrane potentials.
Oct 31, 2023 · Spiking Neural Networks (SNNs) support sparse event-based data processing at high power efficiency when implemented in event-based ...