相关实验视频
在卷积神经网络中的能量精度权衡层特定的近似乘数
Ladan Sayadi1, Mohammad Hossein Moaiyeri2, Somayeh Timarchi1
1Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, 1983969411, Iran.
Scientific reports
|November 11, 2025
概括
本研究引入了对卷积神经网络 (CNN) 的新近方法,显著提高了硬件效率. 该方法优化了近似乘数和训练策略,在CNN设计中实现了大幅度的能源节约.
科学领域:
- 计算机工程 计算机工程
- 人工智能的人工智能
- 硬件加速器 硬件加速器
背景情况:
- 大致计算为神经网络等对错误有弹性的应用程序提供了硬件效率.
- 传统的计算优先考虑精度,往往以效率为代价.
- CNN具有独特的特性,影响了近似技术的适用性.
研究的目的:
- 开发和评估一种新的CNN特定的近似方法.
- 为了提高CNN的硬件效率和节能.
- 在深度学习硬件中平衡计算复杂性和准确性.
主要方法:
- 根据重量分布,确定了适用于CNN的近似乘数的特征.
- 设计了新的近似乘法器 (AM_5×5,AM_4×4,AM_3×3) 使用可调准确度的操作数截断.
- 提出了两种训练算法:一种用于直接优化,另一种使用渐进式策略.
- 通过使用VGG16,VGG10和AlexNet在28纳米CMOS技术中实施和评估ASIC的方法.
主要成果:
- 实现了每项操作的显著能源效率提升:高达86% (VGG10),95% (VGG16) 和88% (AlexNet) 的第一个战略.
- 第二个策略带来了81% (VGG10),92% (VGG16) 和84% (AlexNet) 的能源效率改善.
- 通过操作位位截断证明了近似乘数的可扩展性和可调整的准确性.
结论:
- 拟议的方法有效地利用CNN功能来提高硬件效率.
- 实验结果验证了CNN设计中优化能源和硬件资源的潜力.
- 这种方法通过提高其能源和计算效率来推进CNN的实际应用.
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