VSAP Lab

Neuromorphic Hardware

KU and NYU Collaboration on Neuromorphic Computing

Neuromorphic chips, algorithms, and hardware architectures for efficient AI and robotics.

This collaborative project between KU and NYU explores neuromorphic hardware that mimics neural architecture and functioning of the human brain. Focus areas include development of neuromorphic chips, algorithm optimization for neuromorphic systems, applications in AI and robotics, and interdisciplinary research combining neuroscience, computer science, and electrical engineering.

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Neuromorphic Hardware

Related Publications

F. Renda, A. Mathew and D. F. Talegon, "Dynamics and Control of Soft Robots With Implicit Strain Parametrization," IEEE Robotics and Automation Letters, vol. 9, no. 3, pp. 2782-2789, March 2024.

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P. Pustina, C. D. Santina, F. Boyer, A. De Luca and F. Renda, "Input Decoupling of Lagrangian Systems via Coordinate Transformation," IEEE Transactions on Robotics, 2024.

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Z. Chen et al., "Data-Driven Methods Applied to Soft Robot Modeling and Control: A Review," IEEE Transactions on Automation Science and Engineering, 2024.

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Waseem Akram, Taimur Hassan, Hamed Toubar, Muhayyuddin Ahmed, Nikola Miskovic, Lakmal Seneviratne, and Irfan Hussain, "Aquaculture defects recognition via multi-scale semantic segmentation," Expert Systems with Applications 237, 2024.

Bakht, A.B. et al., "MuLA-GAN: Multi-level attention GAN for enhanced underwater visibility," Ecological Informatics, 2024.

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