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Strained-topological-insulator spin-orbit-torque random-access-memory bit cell for energy-efficient processing in memory

https://doi.org/10.1103/PhysRevApplied.23.014071

Morshed, Md Golam; Vakili, Hamed; Sakib, Mohammad Nazmus; Ganguly, Samiran; Stan, Mircea R; Ghosh, Avik W (January 2025, Physical Review Applied)

Free, publicly-accessible full text available January 1, 2026
Exploring Noise- Resilient Spiking Neural Encoding using ∑†∑ Neurons

https://doi.org/10.1109/MWSCAS60917.2024.10658863

Sreekumar, R; Mullick, Faiyaz E; Morshed, Md Golam; Ghosh, Avik W; Stan, Mircea R (August 2024, IEEE)

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SRAM based opportunistic energy efficiency improvement in dual-supply near-threshold processors

https://doi.org/10.1145/3195970.3196121

Gu, Yunfei; Yan, Dengxue; Verma, Vaibhav; Stan, Mircea R.; Zhang, Xuan (January 2018, Design Automation Conference)

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Hardware based spatio-temporal neural processing backend for imaging sensors: Towards a smart camera

https://doi.org/10.1117/12.2305137

Gu, Yunfei; Ganguly, Samiran; Stan, Mircea R.; Ghosh, Avik W.; Dhar, Nibir K.; Dutta, Achyut K. (May 2018, Image Sensing Technologies: Materials, Devices, Systems, and Applications V)

In this work we show how we can build a technology platform for cognitive imaging sensors using recent advances in recurrent neural network architectures and training methods inspired from biology. We demonstrate learning and processing tasks specific to imaging sensors, including enhancement of sensitivity and signal-to-noise ratio (SNR) purely through neural filtering beyond the fundamental limits sensor materials, and inferencing and spatio-temporal pattern recognition capabilities of these networks with applications in object detection, motion tracking and prediction. We then show designs of unit hardware cells built using complementary metal-oxide semiconductor (CMOS) and emerging materials technologies for ultra-compact and energy-efficient embedded neural processors for smart cameras.
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