一个混合精度的memristor和SRAM内存计算AI处理器
Win-San Khwa1, Tai-Hao Wen2, Hung-Hsi Hsu1,2
1Taiwan Semiconductor Manufacturing Company Limited (TSMC), Hsinchu, Taiwan, Republic of China.
Nature
|March 5, 2025
概括
本研究介绍了一种用于AI边缘设备的新型混合精度异质内存计算 (CIM) 处理器. 它通过在不同的CIM架构和数字格式中智能分区任务来优化能源效率,准确性和速度.
科学领域:
- 计算机工程
- 人工智能硬件
- 节能计算
背景情况:
- 人工智能边缘设备需要高精度,节能计算,大量存储和快速响应时间.
- 使用同质架构 (memristor-CIM,SRAM-CIM) 或计算格式 (整数,浮点) 的传统内存计算 (CIM) 方法面临效率,存储,延迟和准确性的权衡.
- 现有的解决方案难以满足边缘设备上现代人工智能应用的苛刻要求.
研究的目的:
- 开发一种混合精度异质CIMAI边缘处理器,克服传统同质设计的局限性.
- 为了使网络层在不同的芯片 CIM 架构和计算格式之间进行层级/内核级分区.
- 在能源效率,存储,唤醒延迟和推断准确性方面实现同时优化.
主要方法:
- 实现一个整合memristor-CIM,SRAM-CIM和数字单元的异质CIM架构.
- 开发一个灵活的AI网络层分层/核心分层系统.
- 基于错误灵敏度分析的混合精度计算 (整数和浮点) 的支持.
主要成果:
- 实现了高能源效率: 40.91 TFLOPS/W (ResNet-20) 和 28.63 TFLOPS/W (移动网络-v2).
- 在ResNet-20和MobileNet-v2中显示的低精度降解:<0.45%.
- 达到了373.52微秒的快速唤醒响应时间.
结论:
- 拟议的混合精度异质CIM处理器有效地平衡了AI边缘设备的能效,准确性和速度.
- 层级/内核级分区为各种人工智能工作负载提供了强大的硬件级优化策略.
- 这种方法为下一代人工智能边缘计算提供了具有成本效益的,准备好的解决方案.
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