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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2303.02651 |
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| _version_ | 1866915128425840640 |
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| author | Foster, Patrick Papandroulidakis, Georgios Serb, Alex Prodromakis, Spyros Stathopoulos Themis |
| author_facet | Foster, Patrick Papandroulidakis, Georgios Serb, Alex Prodromakis, Spyros Stathopoulos Themis |
| contents | Recent advances in machine learning and neuro-inspired systems enabled the increased interest in efficient pattern recognition at the edge. A wide variety of applications, such as near-sensor classification, require fast and low-power approaches for pattern matching through the use of associative memories and their more well-known implementation, Content Addressable Memories (CAMs). Towards addressing the need for low-power classification, this work showcases an RRAM-based analogue CAM (ACAM) intended for template matching applications, providing a low-power reconfigurable classification engine for the extreme edge. The circuit uses a low component count at 6T2R2M, comparable with the most compact existing cells of this type. In this work, we demonstrate a hardware prototype, built with commercial off-the-shelf (COTS) components for the MOSFET-based circuits, that implements rows of 6T2R2M employing TiOx-based RRAM devices developed in-house, showcasing competitive matching window configurability and definition. Furthermore, through simulations, we validate the performance of the proposed circuit by using a commercially available 180nm technology and in-house RRAM data-driven model to assess the energy dissipation, exhibiting 60 pJ per classification event. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_02651 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | An RRAM-Based Implementation of a Template Matching Circuit for Low-Power Analogue Classification Foster, Patrick Papandroulidakis, Georgios Serb, Alex Prodromakis, Spyros Stathopoulos Themis Signal Processing Recent advances in machine learning and neuro-inspired systems enabled the increased interest in efficient pattern recognition at the edge. A wide variety of applications, such as near-sensor classification, require fast and low-power approaches for pattern matching through the use of associative memories and their more well-known implementation, Content Addressable Memories (CAMs). Towards addressing the need for low-power classification, this work showcases an RRAM-based analogue CAM (ACAM) intended for template matching applications, providing a low-power reconfigurable classification engine for the extreme edge. The circuit uses a low component count at 6T2R2M, comparable with the most compact existing cells of this type. In this work, we demonstrate a hardware prototype, built with commercial off-the-shelf (COTS) components for the MOSFET-based circuits, that implements rows of 6T2R2M employing TiOx-based RRAM devices developed in-house, showcasing competitive matching window configurability and definition. Furthermore, through simulations, we validate the performance of the proposed circuit by using a commercially available 180nm technology and in-house RRAM data-driven model to assess the energy dissipation, exhibiting 60 pJ per classification event. |
| title | An RRAM-Based Implementation of a Template Matching Circuit for Low-Power Analogue Classification |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2303.02651 |