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. Further analysis of scalar addition, multiplication, and matrix multiplication shows promising results. These results are then applied to a complete application: a neural network based handwritten digit classifier, as an example to show the connection between the application performance and further MTJ development. The classifier achieved almost-perfect classification accuracy, with reasonable projections of future MTJ development. 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（尤其是机器智能领域）快速增长的性能需求，其根本原因在于逻辑模块与存储模块之间频繁的数据传输消耗了大量功耗与能量。为突破这一根本性限制，一种名为"计算随机存取存储器"（CRAM）的新范式应运而生。CRAM直接利用存储单元本身执行逻辑运算，无需数据离开存储单元。以往数值研究已充分证实CRAM在传统与新兴应用中的能耗与性能优势。然而，目前尚缺乏对CRAM计算精度这一决定其技术可行性与竞争力的现实关键指标的实验验证与系统性研究。本工作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及二输入、三输入与五输入逻辑运算，随后展示了两种不同设计的1位全加器。基于实验结果，建立了一套模型以表征CRAM计算精度。对标量加法、乘法和矩阵乘法的进一步分析展现了令人鼓舞的结果。最终将这些结果应用于完整应用场景：基于神经网络的手写数字分类器，以此示例展示应用性能与MTJ技术发展的关联性。分类器在合理预测未来MTJ发展水平的前提下，实现了近乎完美的分类精度。MTJ-CRAM计算精度的确证表明，该技术将对机器智能等功耗与能耗密集型应用产生深远影响。