模拟用于非易失性内存的全数字内存计算,用于人工智能边缘应用程序.
Hongyang Hu1,2, Chuancai Feng3, Haiyang Zhou1,2
1State Key Laboratory of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China.
Micromachines
|June 28, 2023
概括
本研究介绍了用于AI边缘推断的数字非挥发性内存计算 (CIM) 宏. 这种新的设计实现了高能效,证明了它对先进的人工智能应用的潜力.
科学领域:
- * 电气工程 电气工程
- * 计算机架构 计算机架构
- * 人工智能 硬件 硬件
背景情况:
- * 数字内存计算 (CIM) 对于高效的AI边缘推断至关重要.
- *使用非易失性内存 (NVM) 的数字CIM需要解决复杂的设备物理.
- *现有的基于NVM的CIM解决方案往往忽视了内在设备特征.
研究的目的:
- * 提出一个全数字非挥发性CIM (DNV-CIM) 宏观.
- * 集成一个压缩编码查找表乘法器 (CCLUTM) 以提高性能.
- * 开发一个适合机器学习的连续积累方案.
主要方法:
- *使用40nm技术实现DNV-CIM宏.
- * 集成了一个新的压缩编码查找表乘法器 (CCLUTM).
- * 应用连续积累方案用于机器学习任务.
- * 在CIFAR-10数据集上使用修改后的ResNet18网络进行模拟.
主要成果:
- * 拟议的基于CCLUTM的DNV-CIM宏显示了与标准NOR闪存的高度兼容性.
- * 在4位乘法和积累 (MAC) 操作中实现了75.18 TOPS/W的峰值能效.
- * 在修改后的ResNet18网络上对图像分类的性能进行了验证.
结论:
- *开发的DNV-CIM宏为节能AI边缘推理提供了一个有前途的解决方案.
- *CCLUTM设计有效地解决了使用NVM的数字CIM的挑战.
- * 连续积累方案提高了机器学习工作负载中的应用性.
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