电阻随机存取存储器
计算机科学
瓶颈
可扩展性
冯·诺依曼建筑
内存处理
计算机体系结构
计算
内存体系结构
并行计算
数据流
电阻式触摸屏
数据流体系结构
编译程序
嵌入式系统
电压
工程类
操作系统
电气工程
算法
情报检索
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作者
Yuyi Liu,Bin Gao,Jianshi Tang,Huaqiang Wu,He Qian
标识
DOI:10.1007/s11432-023-3785-8
摘要
Computation-in-memory (CIM) chips offer an energy-efficient approach to artificial intelligence computing workloads. Resistive random-access memory (RRAM)-based CIM chips have proven to be a promising solution for overcoming the von Neumann bottleneck. In this paper, we review our recent studies on the architecture-circuit-technology co-optimization of scalable CIM chips and related hardware demonstrations. To further minimize data movements between memory and computing units, architecture optimization methods have been introduced. Then, we propose a device-architecture-algorithm co-design simulator to provide guidelines for designing CIM systems. A physics-based compact RRAM model and an array-level analog computing model were embedded in the simulator. In addition, a CIM compiler was proposed to optimize the on-chip dataflow. Finally, research perspectives are proposed for future development.
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