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一个滑动内核计算内存架构用于卷积神经网络
Yushen Hu1, Xinying Xie1, Tengteng Lei1
1State Key Laboratory of Advanced Displays and Optoelectronics Technologies, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology (HKUST), Hong Kong, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 22, 2024
概括
一种新的滑动内核内存计算 (SKCIM) 架构可以将内存访问量减少88%. 这种神经形态计算方法在使用卷积神经网络的手写数字分类中实现了超过95%的准确性.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
背景情况:
- 神经形态计算架构旨在模仿人类大脑的效率.
- 传统系统由于存储器和处理单元之间的数据移动而面临瓶.
- 卷积运算是深度学习的基础,但计算密集.
研究的目的:
- 引入一种新的滑动内核内存计算 (SKCIM) 架构.
- 为了利用低温金属氧化物薄膜晶体管 (TFT) 技术实现单体集成.
- 为了证明SKCIM在卷积任务和神经网络应用中的效率和准确性.
主要方法:
- 设计了一个SKCIM架构,两个重叠的功能数组用于内存和内核存储.
- 利用低温金属氧化物薄膜晶体管 (TFT) 技术用于设备制造.
- 实现了用于卷积任务的 32x32 SKCIM 系统和用于 MNIST 分类的 5 层卷积神经网络.
主要成果:
- 与现有系统相比,实现了88%的内存访问操作减少.
- 在 32x32 SKCIM 系统上成功执行了常见的卷积任务.
- 在使用基于SKCIM的CNN的MNIST手写数字数据集上达到超过95%的准确率.
结论:
- 在深度学习任务中,SKCIM架构为计算效率提供了显著的改进.
- 使用低温TFT技术的单立体集成使先进的神经形态系统的实际实施成为可能.
- SKCIM显示出加速人工智能和机器学习应用的巨大潜力.
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