FACULTY

Adaptive Neuromorphic Prosthetics represent a groundbreaking fusion of neuroscience and artificial intelligence, leveraging advanced algorithms and methods developed within the realm of neuromorphic systems. By emulating the brain's neural networks, these prosthetics achieve real-time responsiveness, energy efficiency, and adaptability to various user needs and environmental conditions. We utilize spiking neural networks and other neuromorphic computing techniques to enable more natural and intuitive control of prosthetic limbs. Our approach enhances the user's experience by providing smoother movements, quicker adjustments, and improved integration with biological systems. We focus on non-invasive technologies that directly interface the environment with the residual arm or legs, allowing for a more seamless and effective user experience.

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Our research is dedicated to the design and utilization of computers with minimal environmental impact, encompassing efforts to reduce energy consumption, minimize waste, and employ sustainable materials. By integrating cutting-edge technologies and innovative methodologies, we aim to develop solutions that not only enhance the efficiency and functionality of computing systems but also contribute to the preservation of our planet. Our multidisciplinary approach involves collaboration with companies and experts in various fields, ensuring that our findings and implementations are both practical and impactful.

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We are exploring the development of an adaptive ultra-low power neuromorphic chip (NASH) and systems, enhanced by our previously developed fault-tolerant three-dimensional on-chip interconnect technology. The NASH system boasts several features, including an efficient adaptive configuration method that enables the reconfiguration of various SNN parameters such as spike weights, routing, hidden layers, and topology. Additionally, the system incorporates a blend of different deep neural network topologies, an efficient fault-tolerant multicast spike routing algorithm, and an effective on-chip learning mechanism. To demonstrate the performance of the NASH system, we will develop an FPGA implementation and establish a VLSI implementation. The ultimate goal of NASH is to bring brain-inspired processing technology to small-scale embedded sensors and sensor-based devices, such as BCI (EEG/EMG), audio, presence detection, and activity recognition.

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