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Shantanu Chakrabartty on Muck Rack

Shantanu Chakrabartty

As seen in: Nature, arXiv, Frontiers
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Articles

Higher-order neuromorphic Ising machines—autoencoders and Fowler-Nordheim annealers are all you need for scalability

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By Faiek Ahsan, Saptarshi Maiti, Zihao Chen, Jakob Kaiser, Ankita Nandi, Madhuvanthi Srivatsav, Johannes Schemmel, Andreas Andreou, Jason Eshraghian, Shantanu Chakrabartty
| Nature Verified
Abstract We report that an autoencoder-based neuromorphic architecture, combined with Fowler-Nordheim annealing, is sufficient to implement scalable higher-order Ising machines. We show that these machines can consistently produce state-of-the-art solutions with high reliability and with competitive time-to-solution metrics.
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[2510.24975] Maximum-Entropy Analog Computing Approaching ExaOPS-per-Watt Energy-efficiency at the RF-Edge

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By Kareem Rashed Verified, Zhili Xiao, Arun Natarajan, Shantanu Chakrabartty
| arXiv
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[2402.14878] Estimation of Energy-dissipation Lower-bounds for Neuromorphic Learning-in-memory

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By Johannes Leugering, Gert Cauwenberghs, Shantanu Chakrabartty, Faiek Ahsan
| arXiv
[Submitted on 21 Feb 2024 ( v1 ), last revised 19 Aug 2025 (this version, v3)] Title:Estimation of Energy-dissipation Lower-bounds for Neuromorphic Learning-in-memory View a PDF of the paper titled Estimation of Energy-dissipation Lower-bounds for Neuromorphic Learning-in-memory, by Zihao Chen and Faiek Ahsan and Johannes Leugering and Gert Cauwenberghs and Shantanu Chakrabartty View PDF HTML (experimental) Abstract:Neuromorphic or neurally-inspired optimizers rely on local but parallel...
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