lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents-Reference-Cited by-同舟云学术

lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 International Joint Conference on Neural Networks (IJCNN)
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Author:

Wicaksana Putra Rachmad Vidya¹,Shafique Muhammad²

Affiliation:

1. Institute of Computer Engineering, Technische Universität Wien (TU Wien),Vienna,Austria

2. New York University Abu Dhabi (NYUAD),Division of Engineering,Abu Dhabi,United Arab Emirates

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892948.pdf?arnumber=9892948

Reference34 articles.

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2. Drmap: A generic dram data mapping policy for energy-efficient processing of convolutional neural networks;putra;ACM/IEEE 57th Design Automation Conference (DAC),2020

3. Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem

4. Continual lifelong learning with neural networks: A review

5. Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks

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