Historical perspective and opportunity for computing in memory using floating-gate and resistive non-volatile computing including neuromorphic computing
The effort addresses the research activity around the usage of non-volatile memories (NVM) for storage of ‘weights’ in neural networks and the resulting computation through these memory crossbars. In particular, we focus on the CMOS implementations of, and comparisons between, memristor/resistive ra...
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| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Neuromorphic Computing and Engineering |
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| Online Access: | https://doi.org/10.1088/2634-4386/ad9b4a |
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| author | Jennifer Hasler Arindam Basu |
| author_facet | Jennifer Hasler Arindam Basu |
| author_sort | Jennifer Hasler |
| collection | DOAJ |
| description | The effort addresses the research activity around the usage of non-volatile memories (NVM) for storage of ‘weights’ in neural networks and the resulting computation through these memory crossbars. In particular, we focus on the CMOS implementations of, and comparisons between, memristor/resistive random access memory (RRAM) devices, and floating-gate (FG) devices. A historical perspective for illustrating FG and memristor/RRAM devices enables comparison of nonvolatile storage (addressing issues related to resolution, lifetime, endurance etc), feedforward computation (different variants of vector matrix multiplication, tradeoffs between power dissipation and signal to noise ratio etc), programming (addressing issues of selectivity, peripheral circuits, charge trapping etc), and learning algorithms (continuous time LMS or batch update), in these systems. We believe this historical perspective is necessary and timely given the increasing interest in using computation in memory with NVM for a wide variety of memory bound applications. |
| format | Article |
| id | doaj-art-419343ee21184255a5425d9d0b77c226 |
| institution | Kabale University |
| issn | 2634-4386 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Neuromorphic Computing and Engineering |
| spelling | doaj-art-419343ee21184255a5425d9d0b77c2262025-01-08T05:49:44ZengIOP PublishingNeuromorphic Computing and Engineering2634-43862025-01-015101200110.1088/2634-4386/ad9b4aHistorical perspective and opportunity for computing in memory using floating-gate and resistive non-volatile computing including neuromorphic computingJennifer Hasler0https://orcid.org/0000-0002-6866-3156Arindam Basu1Electrical and Computer Engineering (ECE), Georgia Institute of Technology , Atlanta, GA 30332-250, United States of AmericaDepartment of Electrical Engineering, City University of Hong Kong , Kowloon Tong, Hong Kong Special Administrative Region of China, People’s Republic of ChinaThe effort addresses the research activity around the usage of non-volatile memories (NVM) for storage of ‘weights’ in neural networks and the resulting computation through these memory crossbars. In particular, we focus on the CMOS implementations of, and comparisons between, memristor/resistive random access memory (RRAM) devices, and floating-gate (FG) devices. A historical perspective for illustrating FG and memristor/RRAM devices enables comparison of nonvolatile storage (addressing issues related to resolution, lifetime, endurance etc), feedforward computation (different variants of vector matrix multiplication, tradeoffs between power dissipation and signal to noise ratio etc), programming (addressing issues of selectivity, peripheral circuits, charge trapping etc), and learning algorithms (continuous time LMS or batch update), in these systems. We believe this historical perspective is necessary and timely given the increasing interest in using computation in memory with NVM for a wide variety of memory bound applications.https://doi.org/10.1088/2634-4386/ad9b4afloating-gate devicescircuits and systemsmemristorsRRAM |
| spellingShingle | Jennifer Hasler Arindam Basu Historical perspective and opportunity for computing in memory using floating-gate and resistive non-volatile computing including neuromorphic computing Neuromorphic Computing and Engineering floating-gate devices circuits and systems memristors RRAM |
| title | Historical perspective and opportunity for computing in memory using floating-gate and resistive non-volatile computing including neuromorphic computing |
| title_full | Historical perspective and opportunity for computing in memory using floating-gate and resistive non-volatile computing including neuromorphic computing |
| title_fullStr | Historical perspective and opportunity for computing in memory using floating-gate and resistive non-volatile computing including neuromorphic computing |
| title_full_unstemmed | Historical perspective and opportunity for computing in memory using floating-gate and resistive non-volatile computing including neuromorphic computing |
| title_short | Historical perspective and opportunity for computing in memory using floating-gate and resistive non-volatile computing including neuromorphic computing |
| title_sort | historical perspective and opportunity for computing in memory using floating gate and resistive non volatile computing including neuromorphic computing |
| topic | floating-gate devices circuits and systems memristors RRAM |
| url | https://doi.org/10.1088/2634-4386/ad9b4a |
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