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基于电阻随机访问内存的计算内存芯片
Weier Wan1,2, Rajkumar Kubendran3,4, Clemens Schaefer5
1Stanford University, Stanford, CA, USA. weierwan@stanford.edu.
Nature
|August 17, 2022
概括
这项研究介绍了NeuRRAM,一种使用电阻随机存储器 (RRAM) 的新型计算内存 (CIM) 芯片. 对于边缘设备的人工智能 (AI) 任务,NeuRRAM实现了卓越的能源效率和精度.
科学领域:
- 材料科学
- 计算机工程
- 人工智能
背景情况:
- 边缘设备需要节能硬件来实现复杂的AI功能.
- 使用电阻随机存储器 (RRAM) 的内存计算 (CIM) 通过整合内存和计算提供了一个解决方案.
- 现有的RRAM-CIM芯片在平衡能源效率,模型多功能性和准确性方面面临挑战.
研究的目的:
- 开发一个基于RRAM的CIM芯片,NeuRRAM,它克服了效率,多功能性和准确性之间的权衡.
- 在多个设计层次上展示同时的改进:算法,架构,电路和设备.
- 在前所未有的能源效率下直接在边缘设备上实现先进的AI功能.
主要方法:
- 对RRAM-CIM设计的算法,架构,电路和设备进行协同优化.
- 开发一种基于RRAM的新型CIM芯片,名为NeuRRAM.
- 集成密集,模拟,非易失性RRAM设备用于内存计算.
主要成果:
- 与之前的RRAM-CIM芯片相比,NeuRRAM的能效提高了两倍.
- 该芯片通过为各种AI模型架构重新配置CIM核心来展示多功能性.
- 推断的准确性与在各种AI任务中使用四位数量化的软件模型相比较,包括图像分类和语音识别.
结论:
- 在基于RRAM的CIM技术中,NeuRRAM是一个显著的进步.
- 协同优化方法成功地解决了效率-多功能性-准确性权衡问题.
- 这项技术为边缘设备的高效准确人工智能处理铺平了道路.
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