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在FPGA中的二进制神经网络:架构,工具流和硬件比较
Yuanxin Su1,2, Kah Phooi Seng1,3,4, Li Minn Ang3
1School of AI and Advanced Computing, Xi'an Jiaotong Liverpool University, Suzhou 215000, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
二进制神经网络 (BNNs) 通过使用二进制权重为资源有限的设备提供高效的计算. 本综述探讨FPGA硬件的BNN,解决性能挑战和实施策略.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 硬件工程 硬件工程
背景情况:
- 二元神经网络 (BNN) 使用二元权重 {-1,1} 进行高效的计算.
- 通过BNN,可以进行比特式操作,加速训练/推理,减少硬件复杂性.
- 它们适用于资源有限的设备,如FPGA和ASIC,与传统的深度学习模型不同.
研究的目的:
- 提供针对FPGA硬件实现的二进制神经网络 (BNN) 的全面审查.
- 为了解决BNN二进制化固有的性能和准确性权衡问题.
- 调查现有的BNN架构,设计流程和与FPGA相关的应用程序.
主要方法:
- 关于二进制神经网络架构和变体的文献综述.
- 在FPGA上实施BNN的设计和工具流程的分析.
- 审查BNN申请和对FPGA的基准结果.
主要成果:
- 对于硬件部署,BNN在速度和尺寸缩小方面提供了显著的优势.
- 二元化引入了研究人员正在积极缓解的性能/精度权衡.
- 已经开发了各种BNN架构和FPGA实施策略.
结论:
- 在FPGA上,BNN是有效的深度学习的有希望的方法.
- 需要进一步的研究来缩小BNN和全精度网络之间的性能差距.
- 本综述为FPGA硬件的BNN提供了有价值的见解,包括工具和基准.
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