用于可重构计算的现场可编程横杆阵列(FPCA)

Mohammed A. Zidan;YeonJoo Jeong;Jong Hoon Shin;Chao Du;Zhengya Zhang;Wei D. Lu
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引用次数: 34

摘要

几十年来,根据摩尔定律,CMOS晶体管的缩放直接推动了电子技术的进步。然而,CMOS缩放和经典计算机体系结构都接近基本和实际的极限,基于新兴器件(如电阻随机存取存储器(RRAM)器件)的新计算体系结构有望维持计算能力的指数增长。在这里,我们提出了一种新的以内存为中心、可重新配置的通用计算平台,该平台能够以快速高效的方式处理爆炸性的数据量。所提出的计算架构基于统一的、物理的、电阻的、以内存为中心的结构,该结构可以优化地重新配置并用于以大规模并行的方式执行不同的计算和数据存储任务。通过动态分配用于存储、算术和模拟计算(包括神经形态计算任务)的基本计算结构,可以基于数据流对系统进行定制,以实现最大能效。
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Field-Programmable Crossbar Array (FPCA) for Reconfigurable Computing
For decades, advances in electronics were directly driven by the scaling of CMOS transistors according to Moore's law. However, both the CMOS scaling and the classical computer architecture are approaching fundamental and practical limits, and new computing architectures based on emerging devices, such as resistive random-access memory (RRAM) devices, are expected to sustain the exponential growth of computing capability. Here, we propose a novel memory-centric, reconfigurable, general purpose computing platform that is capable of handling the explosive amount of data in a fast and energy-efficient manner. The proposed computing architecture is based on a uniform, physical, resistive, memory-centric fabric that can be optimally reconfigured and utilized to perform different computing and data storage tasks in a massively parallel approach. The system can be tailored to achieve maximal energy efficiency based on the data flow by dynamically allocating the basic computing fabric for storage, arithmetic, and analog computing including neuromorphic computing tasks.
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