在 Loihi 2 神经形态处理器上解决 QUBO 问题

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-08-06 DOI:arxiv-2408.03076

Alessandro Pierro, Philipp Stratmann, Gabriel Andres Fonseca Guerra, Sumedh Risbud, Timothy Shea, Ashish Rao Mangalore, Andreas Wild

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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。

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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.

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arXiv - CS - Neural and Evolutionary Computing

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