Reconfigurable Compute-In-Memory on Field-Programmable Ferroelectric Diodes
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866914944634585088 |
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| author | Liu, Xiwen Ting, John He, Yunfei Fiagbenu, Merrilyn Mercy Adzo Zheng, Jeffrey Wang, Dixiong Frost, Jonathan Musavigharavi, Pariasadat Esteves, Giovanni Kisslinger, Kim Anantharaman, Surendra B. Stach, Eric A. Olsson III, Roy H. Jariwala, Deep |
| author_facet | Liu, Xiwen Ting, John He, Yunfei Fiagbenu, Merrilyn Mercy Adzo Zheng, Jeffrey Wang, Dixiong Frost, Jonathan Musavigharavi, Pariasadat Esteves, Giovanni Kisslinger, Kim Anantharaman, Surendra B. Stach, Eric A. Olsson III, Roy H. Jariwala, Deep |
| contents | The deluge of sensors and data generating devices has driven a paradigm shift in modern computing from arithmetic-logic centric to data-centric processing. Data-centric processing require innovations at device level to enable novel compute-in-memory (CIM) operations. A key challenge in construction of CIM architectures is the conflicting trade-off between the performance and their flexibility for various essential data operations. Here, we present a transistor-free CIM architecture that permits storage, search and neural network operations on sub-50nm thick Aluminum Scandium Nitride ferroelectric diodes (FeDs). Our circuit designs and devices can be directly integrated on top of Silicon microprocessors in a scalable process. By leveraging the field-programmability, non-volatility and non-linearity of FeDs, search operations are demonstrated with a cell footprint < 0.12 um2 when projected onto 45-nm node technology. We further demonstrate neural network operations with 4-bit operation using FeDs. Our results highlight FeDs as candidates for efficient and multifunctional CIM platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_05259 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Reconfigurable Compute-In-Memory on Field-Programmable Ferroelectric Diodes Liu, Xiwen Ting, John He, Yunfei Fiagbenu, Merrilyn Mercy Adzo Zheng, Jeffrey Wang, Dixiong Frost, Jonathan Musavigharavi, Pariasadat Esteves, Giovanni Kisslinger, Kim Anantharaman, Surendra B. Stach, Eric A. Olsson III, Roy H. Jariwala, Deep Applied Physics Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics Materials Science The deluge of sensors and data generating devices has driven a paradigm shift in modern computing from arithmetic-logic centric to data-centric processing. Data-centric processing require innovations at device level to enable novel compute-in-memory (CIM) operations. A key challenge in construction of CIM architectures is the conflicting trade-off between the performance and their flexibility for various essential data operations. Here, we present a transistor-free CIM architecture that permits storage, search and neural network operations on sub-50nm thick Aluminum Scandium Nitride ferroelectric diodes (FeDs). Our circuit designs and devices can be directly integrated on top of Silicon microprocessors in a scalable process. By leveraging the field-programmability, non-volatility and non-linearity of FeDs, search operations are demonstrated with a cell footprint < 0.12 um2 when projected onto 45-nm node technology. We further demonstrate neural network operations with 4-bit operation using FeDs. Our results highlight FeDs as candidates for efficient and multifunctional CIM platforms. |
| title | Reconfigurable Compute-In-Memory on Field-Programmable Ferroelectric Diodes |
| topic | Applied Physics Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics Materials Science |
| url | https://arxiv.org/abs/2202.05259 |