TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912079654420480 |
|---|---|
| author | Greatorex, Hugh Richter, Ole Mastella, Michele Cotteret, Madison Klein, Philipp Fabre, Maxime Rubino, Arianna Girão, Willian Soares Chen, Junren Ziegler, Martin Bégon-Lours, Laura Indiveri, Giacomo Chicca, Elisabetta |
| author_facet | Greatorex, Hugh Richter, Ole Mastella, Michele Cotteret, Madison Klein, Philipp Fabre, Maxime Rubino, Arianna Girão, Willian Soares Chen, Junren Ziegler, Martin Bégon-Lours, Laura Indiveri, Giacomo Chicca, Elisabetta |
| contents | Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with CMOS circuitry. To address this, we introduce TEXEL, a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning circuits and novel two- and three-terminal devices. TEXEL serves as an accessible platform to bridge the gap between CMOS-based neuromorphic computation and the latest advancements in emerging devices. In this paper, we demonstrate the readiness of TEXEL for device integration through comprehensive chip measurements and simulations. TEXEL provides a practical system for testing bio-inspired learning algorithms alongside emerging devices, establishing a tangible link between brain-inspired computation and cutting-edge device research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15854 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration Greatorex, Hugh Richter, Ole Mastella, Michele Cotteret, Madison Klein, Philipp Fabre, Maxime Rubino, Arianna Girão, Willian Soares Chen, Junren Ziegler, Martin Bégon-Lours, Laura Indiveri, Giacomo Chicca, Elisabetta Neural and Evolutionary Computing Hardware Architecture Emerging Technologies Machine Learning Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with CMOS circuitry. To address this, we introduce TEXEL, a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning circuits and novel two- and three-terminal devices. TEXEL serves as an accessible platform to bridge the gap between CMOS-based neuromorphic computation and the latest advancements in emerging devices. In this paper, we demonstrate the readiness of TEXEL for device integration through comprehensive chip measurements and simulations. TEXEL provides a practical system for testing bio-inspired learning algorithms alongside emerging devices, establishing a tangible link between brain-inspired computation and cutting-edge device research. |
| title | TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration |
| topic | Neural and Evolutionary Computing Hardware Architecture Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2410.15854 |