在 Loihi 2 神经形态处理器上解决 QUBO 问题

Alessandro Pierro, Philipp Stratmann, Gabriel Andres Fonseca Guerra, Sumedh Risbud, Timothy Shea, Ashish Rao Mangalore, Andreas Wild
{"title":"在 Loihi 2 神经形态处理器上解决 QUBO 问题","authors":"Alessandro Pierro, Philipp Stratmann, Gabriel Andres Fonseca Guerra, Sumedh Risbud, Timothy Shea, Ashish Rao Mangalore, Andreas Wild","doi":"arxiv-2408.03076","DOIUrl":null,"url":null,"abstract":"In this article, we describe an algorithm for solving Quadratic Unconstrained\nBinary Optimization problems on the Intel Loihi 2 neuromorphic processor. The\nsolver is based on a hardware-aware fine-grained parallel simulated annealing\nalgorithm developed for Intel's neuromorphic research chip Loihi 2. Preliminary\nresults show that our approach can generate feasible solutions in as little as\n1 ms and up to 37x more energy efficient compared to two baseline solvers\nrunning on a CPU. These advantages could be especially relevant for size-,\nweight-, and power-constrained edge computing applications.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":"41 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Solving QUBO on the Loihi 2 Neuromorphic Processor\",\"authors\":\"Alessandro Pierro, Philipp Stratmann, Gabriel Andres Fonseca Guerra, Sumedh Risbud, Timothy Shea, Ashish Rao Mangalore, Andreas Wild\",\"doi\":\"arxiv-2408.03076\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, we describe an algorithm for solving Quadratic Unconstrained\\nBinary Optimization problems on the Intel Loihi 2 neuromorphic processor. The\\nsolver is based on a hardware-aware fine-grained parallel simulated annealing\\nalgorithm developed for Intel's neuromorphic research chip Loihi 2. Preliminary\\nresults show that our approach can generate feasible solutions in as little as\\n1 ms and up to 37x more energy efficient compared to two baseline solvers\\nrunning on a CPU. These advantages could be especially relevant for size-,\\nweight-, and power-constrained edge computing applications.\",\"PeriodicalId\":501347,\"journal\":{\"name\":\"arXiv - CS - Neural and Evolutionary Computing\",\"volume\":\"41 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Neural and Evolutionary Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.03076\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Neural and Evolutionary Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.03076","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文介绍了一种在英特尔 Loihi 2 神经形态处理器上解决二次无约束优化问题的算法。该算法基于为英特尔神经形态研究芯片 Loihi 2 开发的硬件感知细粒度并行模拟退火算法。初步结果表明,我们的方法能在 1 毫秒内生成可行的解决方案,与在 CPU 上运行的两个基线求解器相比,能效最高可提高 37 倍。这些优势对于尺寸、重量和功耗受限的边缘计算应用尤为重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Solving QUBO on the Loihi 2 Neuromorphic Processor
In this article, we describe an algorithm for solving Quadratic Unconstrained Binary Optimization problems on the Intel Loihi 2 neuromorphic processor. The solver is based on a hardware-aware fine-grained parallel simulated annealing algorithm developed for Intel's neuromorphic research chip Loihi 2. Preliminary results show that our approach can generate feasible solutions in as little as 1 ms and up to 37x more energy efficient compared to two baseline solvers running on a CPU. These advantages could be especially relevant for size-, weight-, and power-constrained edge computing applications.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Hardware-Friendly Implementation of Physical Reservoir Computing with CMOS-based Time-domain Analog Spiking Neurons Self-Contrastive Forward-Forward Algorithm Bio-Inspired Mamba: Temporal Locality and Bioplausible Learning in Selective State Space Models PReLU: Yet Another Single-Layer Solution to the XOR Problem Inferno: An Extensible Framework for Spiking Neural Networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1