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 lacks an experimental demonstration and study of CRAM to evaluate its computation accuracy, which is a realistic and application-critical metrics 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 modeling 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.

翻译：传统计算范式难以满足新兴应用（尤其是机器智能应用）快速增长的需求，其主要原因在于逻辑模块与存储模块间的持续数据传输消耗了大量功率与能量。一种称为"计算随机存取存储器（CRAM）"的新范式应运而生，旨在解决这一根本性限制。CRAM直接利用存储单元自身执行逻辑运算，无需数据离开存储器。先前数值研究已充分证实了CRAM在传统应用和新兴应用中带来的能效与性能优势。然而，目前仍缺乏针对CRAM计算精度的实验验证与研究，而计算精度是评估其技术可行性与竞争力的现实关键指标。本研究通过实验验证了基于磁隧道结（MTJ）的CRAM阵列。首先，研究了基本存储操作以及2输入、3输入和5输入逻辑运算。随后，演示了两种不同设计的1位全加器。基于实验结果，我们开发了一套建模方法来表征CRAM的计算精度。对许多传统及机器智能应用的核心构建模块——标量加法、乘法和矩阵乘法进行了评估，结果显示其精度表现优异。随着MTJ基CRAM精度的确认，有充分理由相信该技术将对机器智能中高功耗与高能耗需求的应用产生重大影响。