Tiny classifier circuits as accelerators for classification of tabular data
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Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41928-024-01166-4.pdf
Reference5 articles.
1. Biggs, J. et al. A natively flexible 32-bit Arm microprocessor. Nature 595, 532–536 (2021). This paper introduced an Arm microprocessor fabricated using metal–oxide thin-film transistor technology on flexible substrates, which paved the way for the development of low-cost and non-silicon integrated systems.
2. Ozer, E. et al. Bespoke machine learning processor development framework on flexible substrates. In 2019 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) 1–3 (IEEE, 2019). This paper proposes a framework for the fast design of ML processors on flexible substrates, with rapid development and fabrication cycles that are key for the design of processors for FMCG markets.
3. Ozer, E. et al. A hardwired machine learning processing engine fabricated with submicron metal-oxide thin-film transistors on a flexible substrate. Nat. Electron. 3, 419–425 (2020). This paper presents a processing engine fabricated on flexible substrates for odour recognition using ML.
4. Ozer, E. et al. Binary neural network as a flexible integrated circuit for odour classification. In 2020 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS) 1–4 (IEEE, 2020). This paper sets out the development of binary neural networks on flexible substrates for odour classification.
5. Ozer, E. et al. Malodour classification with low-cost flexible electronics. Nat. Commun. 14, 777 (2023). This paper reports a decision-tree-based ML model that is trained to assess the strength of human odour.
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