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探索尖端神经网络中的权衡

Florian Bacho¹, Dominique Chu²

¹CEMS, School of Computing, University of Kent, Canterbury CT2 7NF, U.K. fb320@kent.ac.uk.

Neural computation

|July 31, 2023

概括

尖端神经网络 (SNN) 提供低功耗计算,但面临时间至第一个尖端 (TTFS) 等约束的权衡. 放松这种约束可以提高性能,速度和稳定性,有利于神经形态计算的发展.

科学领域:

神经形态计算是一种神经形态计算.
人工智能的人工智能
计算神经科学是一种神经科学.

背景情况:

尖端神经网络 (SNN) 对低功耗计算具有前景,为传统深度神经网络提供了替代方案.
无线网络的有效性取决于性能,能源消耗,速度和稳定性.
像Fast & Deep这样的当前方法使用时间到第一个峰值 (TTFS) 约束来提高效率,但这限制了SNN功能.

研究的目的:

在TTFS约束下研究SNN在性能,能源消耗,速度和稳定性之间的权衡.
提出和评估Fast & Deep方法的放松版本,允许每个神经元的多个尖峰.
为了证明不受约束的SNN对TTFSSNN的优势,以有效的学习策略.

主要方法:

探索TTFS SNNs中的性能,能源消耗,速度和稳定性权衡.
一个放松的快速深度模型的建议,允许每个神经元的多个尖峰.
实验性比较TTFS SNNs与关键绩效指标的建议宽松模型.

主要成果:

由于TTFS的限制造成了权衡,牺牲了稀疏性,并增加了对性能和稳定性的延迟.
在Fast & Deep中放松尖峰约束导致了更高的性能和更快的融合.
与TTFS SNNs相比,放松模型表现出类似的稀疏性,可比的延迟时间和更好的噪声稳定性.

结论:

在SNN中,TTFS约束带来了重要的限制,可以通过放松尖峰限制来克服这些限制.
不受约束的SNN,特别是拟议的宽松的Fast & Deep模型,提供卓越的性能,效率和稳定性.
这项研究为开发神经形态计算中的高级学习策略提供了关键的见解.

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