Lv Yang, Zink Brandon R, Bloom Robert P, Cılasun Hüsrev, Khanal Pravin, Resch Salonik, Chowdhury Zamshed, Habiboglu Ali, Wang Weigang, Sapatnekar Sachin S, Karpuzcu Ulya, Wang Jian-Ping
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA.
Department of Physics, University of Arizona, Tucson, Arizona 85721 USA.
Npj Unconv Comput. 2024;1(1):3. doi: 10.1038/s44335-024-00003-3. Epub 2024 Jul 25.
The conventional computing paradigm struggles to fulfill the rapidly growing demands from emerging applications, especially those for machine intelligence because much of the power and energy is consumed by constant data transfers between logic and memory modules. A new paradigm, called "computational random-access memory (CRAM)," has emerged to address this fundamental limitation. CRAM performs logic operations directly using the memory cells themselves, without having the data ever leave the memory. The energy and performance benefits of CRAM for both conventional and emerging applications have been well established by prior numerical studies. However, there is a lack of experimental demonstration and study of CRAM to evaluate its computational accuracy, which is a realistic and application-critical metric for its technological feasibility and competitiveness. In this work, a CRAM array based on magnetic tunnel junctions (MTJs) is experimentally demonstrated. First, basic memory operations, as well as 2-, 3-, and 5-input logic operations, are studied. Then, a 1-bit full adder with two different designs is demonstrated. Based on the experimental results, a suite of models has been developed to characterize the accuracy of CRAM computation. Scalar addition, multiplication, and matrix multiplication, which are essential building blocks for many conventional and machine intelligence applications, are evaluated and show promising accuracy performance. With the confirmation of MTJ-based CRAM's accuracy, there is a strong case that this technology will have a significant impact on power- and energy-demanding applications of machine intelligence.
传统计算范式难以满足新兴应用快速增长的需求，尤其是对机器智能应用的需求，因为逻辑模块和内存模块之间持续的数据传输消耗了大量的功率和能量。一种名为“计算随机存取存储器（CRAM）”的新范式应运而生，以解决这一根本限制。CRAM直接利用存储单元本身执行逻辑运算，而无需数据离开存储器。先前的数值研究已经充分证实了CRAM在传统应用和新兴应用方面的能量和性能优势。然而，目前缺乏对CRAM的实验论证和研究来评估其计算准确性，而计算准确性对于其技术可行性和竞争力而言是一个现实且对应用至关重要的指标。在这项工作中，通过实验展示了一种基于磁性隧道结（MTJ）的CRAM阵列。首先，研究了基本的存储操作以及二输入、三输入和五输入逻辑操作。然后，展示了具有两种不同设计的1位全加器。基于实验结果，开发了一套模型来表征CRAM计算的准确性。标量加法、乘法和矩阵乘法是许多传统应用和机器智能应用的基本构建块，对其进行评估后显示出了有前景的准确性表现。随着基于MTJ的CRAM准确性得到证实，有充分理由相信这项技术将对机器智能中对功率和能量要求较高的应用产生重大影响。
