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在FPGA上的尖端神经网络:对方法和最近进展的调查

Mehrzad Karamimanesh¹, Ebrahim Abiri¹, Mahyar Shahsavari²

¹Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran.

Neural networks : the official journal of the International Neural Network Society

|February 18, 2025

概括

尖端神经网络 (SNN) 模仿大脑,以降低功耗. 现场可编程门阵列 (FPGA) 为开发这些先进的SNN提供了高效的硬件.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
电气工程电气工程

背景情况:

尖端神经网络 (SNN) 显示出通过模仿生物大脑来实现低功耗,高速信息处理的前景.
传统的计算系统面临着对复杂的人工智能任务的功率效率和速度的限制.
开发由大脑启发的计算架构是一个关键的研究前沿.

研究的目的:

审查最近在尖端神经网络 (SNN) 研究方面的进展.
评估现场可编程网关数组 (FPGA) 作为适合SNN实现的硬件.
确定阻碍在FPGA上开发和部署SNN的挑战.

主要方法:

关于SNN架构及其功率效率的综合文献综述.
对FPGAs与SNN执行的CPU和GPU进行比较分析.
对在FPGA平台上实施SNN的现有研究进行了审查.

主要成果:

FPGAs提供并行处理,低延迟和更低的功耗,使它们成为SNNs的理想选择.
通过SNN,可以显著节省电力和提高识别能力.
目前的研究突出了SNN开发和FPGA集成的早期阶段.

结论:

FPGAs是非常适合加速SNN研究和开发的硬件.
克服FPGA的实施挑战对于实现SNN的全部潜力至关重要.
需要进行进一步的研究,以优化SNN在FPGA上的大脑效率和性能.

关键词:

加速器加速器的加速器是什么?大脑启发的计算现场可编程的门阵列.神经形态计算是一种神经形态计算.尖的神经网络的神经网络.

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