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一种线性补偿方法,用于推断准确度的提高,用于内存计算的内存计算.
Yuehua Dai1, Zeqing Wang1, Zhe Feng1
1School of Integrated Circuits, Anhui University, Hefei, Anhui 230601, People's Republic of China.
Nanotechnology
|August 29, 2024
概括
一种新的线性补偿方法 (LCM) 通过解决非理想性来改进记忆计算系统 (MCS). 这种方法提高了人工神经网络 (ANN) 在硬件中的性能,为更强大的基于memristor的计算铺平了道路.
科学领域:
- 计算机工程 计算机工程
- 材料科学 材料科学 材料科学
背景情况:
- 记忆式计算系统 (MCS) 为人工神经网络 (ANN) 提供低功耗和高并行性,为传统·诺伊曼架构提供了替代方案.
- 外围电路和memristor阵列中的非理想性显著降低了实际MCS的性能.
研究的目的:
- 提出和验证一个线性补偿方法 (LCM) 以提高MCS在非理想条件下的性能.
- 为了研究和建模MCS的输出误差,考虑各种非理想状态.
主要方法:
- 基于实验数据建立了一个输出误差的数学模型,考虑MCS作为一个整体.
- 进行了物理电路层面的分析,以了解非理想因素如何影响输出电流.
- 拟议的LCM使用已建立的数学模型实时补偿输出电流.
主要成果:
- 简单代码有效地弥补了MCS的非理想性,显著提高了系统性能.
- 使用ResNet-34网络架构展示了卓越的性能,该架构对硬件非理想性敏感.
- 该LCM无集成到MCS操作中,增强在通用ANN硬件上的部署.
结论:
- 拟议的LCM是一个可行的解决方案,用于缓解memristive计算系统中的非理想性.
- 这种方法增强了MCS对ANN的实际适用性,特别是在硬件实现中.
- 基于LCM的方法有助于优化基于memristor的ANN硬件.
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