完全连接的人工神经网络的节能硬件实现,使用近似的算术块.
Mohammadreza Esmali Nojehdeh1, Mustafa Altun1
1Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey.
概括
本研究介绍了使用近似计算的人工神经网络 (ANN) 的高效硬件. 大致的加法和乘法在ANN中显著降低了面积和能源消耗.
科学领域:
- 计算机工程 计算机工程
- 人工智能 硬件 硬件
背景情况:
- 人工神经网络 (ANN) 需要大量的硬件资源,特别是平行架构.
- 时间复合架构用于管理ANN的区域要求,通过在MAC块中重复使用计算资源来管理ANN的区域要求.
研究的目的:
- 探索使用近似加法器和乘法器的前ANN的高效硬件实现.
- 根据所需的准确性,开发一种算法来确定乘数和加数的适当近似水平.
- 评估近似计算对ANN硬件效率的影响.
主要方法:
- 使用时间复数架构实现的ANN,在MAC块中使用近似的加数和乘数.
- 开发并应用了一种算法来设置硬件组件的近似级别.
- 在使用各种ANN架构的MNIST和SVHN数据集上测试了拟议的方法.
主要成果:
- 与拟议的近似乘法器设计的ANN显示,与现有的近似乘法器相比,面积和能源消耗减少.
- 使用近似的加数和乘数导致能源消耗减少多达50%,面积减少10%.
- 这些设计保持了高硬件精度,与精确计算的偏差最小.
结论:
- 近似计算为提高ANN硬件实现效率提供了一个可行的策略.
- 拟议的方法有效地减少了ANN的面积和能源足迹,而不会造成实质性的准确性损失.
- 这种方法有利于在资源有限的环境中部署ANN.
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