Phase-change materials for energy-efficient photonic memory and computing
Neuromorphic algorithms achieve remarkable performance milestones in tasks where humans have traditionally excelled. The breadth of data generated by these paradigms is, however, unsustainable by conventional computing chips. In-memory computing hardware aims to mimic biological neural networks and...
| Main Authors: | , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer Nature
2022
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| _version_ | 1826313897624207360 |
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| author | Zhou, W Farmakidis, N Feldmann, J Li, X Tan, J He, Y Wright, CD Pernice, WHP Bhaskaran, H |
| author_facet | Zhou, W Farmakidis, N Feldmann, J Li, X Tan, J He, Y Wright, CD Pernice, WHP Bhaskaran, H |
| author_sort | Zhou, W |
| collection | OXFORD |
| description | Neuromorphic algorithms achieve remarkable performance milestones in tasks where humans have traditionally excelled. The breadth of data generated by these paradigms is, however, unsustainable by conventional computing chips. In-memory computing hardware aims to mimic biological neural networks and has emerged as a viable path in overcoming fundamental limitations of the von Neumann architecture. By eliminating the latency and energy losses associated with transferring data between the memory and central processing unit (CPU), these systems promise to improve on both speed and energy. Photonic implementations using on-chip, nonvolatile memories are particularly promising as they aim to deliver energy-efficient, high-speed, and high-density data processing within the photonic memory with the multiplexing advantages of optics. In this article, we overview recent progress in this direction that integrates phase-change material (PCM) memory elements with integrated optoelectronics. We compare performances of PCM devices using optoelectronic programming schemes and show that energy consumption can be significantly reduced to 60 pJ using picosecond (ps) optical pulse programming and plasmonic nanogap devices with a programming speed approaching 1 GHz. With these energy-efficient waveguide memories, concepts of in-memory photonic computing are implemented based on crossbar arrays. Compared with digital electronic accelerators: application-specific integrated circuits (ASICs) and graphics processing units (GPUs), photonic cores promise 1−3 orders higher compute density and energy efficiency, although much more work toward commercialization is still required. Graphical abstract: [Figure not available: see fulltext.]. |
| first_indexed | 2024-09-25T04:23:37Z |
| format | Journal article |
| id | oxford-uuid:73c63724-3d88-435a-bd12-777a2ae14c17 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:23:37Z |
| publishDate | 2022 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:73c63724-3d88-435a-bd12-777a2ae14c172024-08-21T13:05:01ZPhase-change materials for energy-efficient photonic memory and computingJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:73c63724-3d88-435a-bd12-777a2ae14c17EnglishSymplectic ElementsSpringer Nature2022Zhou, WFarmakidis, NFeldmann, JLi, XTan, JHe, YWright, CDPernice, WHPBhaskaran, HNeuromorphic algorithms achieve remarkable performance milestones in tasks where humans have traditionally excelled. The breadth of data generated by these paradigms is, however, unsustainable by conventional computing chips. In-memory computing hardware aims to mimic biological neural networks and has emerged as a viable path in overcoming fundamental limitations of the von Neumann architecture. By eliminating the latency and energy losses associated with transferring data between the memory and central processing unit (CPU), these systems promise to improve on both speed and energy. Photonic implementations using on-chip, nonvolatile memories are particularly promising as they aim to deliver energy-efficient, high-speed, and high-density data processing within the photonic memory with the multiplexing advantages of optics. In this article, we overview recent progress in this direction that integrates phase-change material (PCM) memory elements with integrated optoelectronics. We compare performances of PCM devices using optoelectronic programming schemes and show that energy consumption can be significantly reduced to 60 pJ using picosecond (ps) optical pulse programming and plasmonic nanogap devices with a programming speed approaching 1 GHz. With these energy-efficient waveguide memories, concepts of in-memory photonic computing are implemented based on crossbar arrays. Compared with digital electronic accelerators: application-specific integrated circuits (ASICs) and graphics processing units (GPUs), photonic cores promise 1−3 orders higher compute density and energy efficiency, although much more work toward commercialization is still required. Graphical abstract: [Figure not available: see fulltext.]. |
| spellingShingle | Zhou, W Farmakidis, N Feldmann, J Li, X Tan, J He, Y Wright, CD Pernice, WHP Bhaskaran, H Phase-change materials for energy-efficient photonic memory and computing |
| title | Phase-change materials for energy-efficient photonic memory and computing |
| title_full | Phase-change materials for energy-efficient photonic memory and computing |
| title_fullStr | Phase-change materials for energy-efficient photonic memory and computing |
| title_full_unstemmed | Phase-change materials for energy-efficient photonic memory and computing |
| title_short | Phase-change materials for energy-efficient photonic memory and computing |
| title_sort | phase change materials for energy efficient photonic memory and computing |
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