Experimental demonstration of magnetic tunnel junction-based computational random-access memory
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929365042855936 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_14264 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Experimental demonstration of magnetic tunnel junction-based computational random-access memory 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 Emerging Technologies Mesoscale and Nanoscale Physics Artificial Intelligence Hardware Architecture Systems and Control 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. |
| title | Experimental demonstration of magnetic tunnel junction-based computational random-access memory |
| topic | Emerging Technologies Mesoscale and Nanoscale Physics Artificial Intelligence Hardware Architecture Systems and Control |
| url | https://arxiv.org/abs/2312.14264 |