FACULTY

Neuromorphic computing uses Spiking Neuron Network models to solve machine learning problems in a more power/energy-efficient way when compared to the conventional Artificial Neural Networks.
This project aims to research and develop an adaptive low-power spiking neural network system in hardware (NASH) empowered with our earlier developed fault-tolerant three-dimensional on-chip interconnect technology. The NASH system features the following: (1) An efficient adaptive configuration method to enable the reconfiguration of different SNN parameters (spike weights, routing, hidden layers, topology, etc.), (2) A mixture of different deep NN topologies, (3) An efficient fault-tolerant multicast spike routing algorithm, (4) An efficient on-chip learning mechanism.
To demonstrate the performance of the NASH system, an FPGA implementation shall be developed, and a VLSI implementation shall also be established.

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Complex SoCs contain dozens of components made of processor cores, DSPs, memory, accelerators, and I/O, all integrated into a single die area of just a few square millimeters. Such complex systems will be interconnected via a complex on-chip interconnect closer to a sophisticated network than current bus-based solutions. This network must provide high throughput and low latency while keeping area and power consumption low. Our research effort is about solving several design challenges to enable such new paradigm in massively parallel many-core systems. In particular, we are investigating fault-tolerance, 3D-TSV integration, photonic communication, low-power mapping techniques, and low-latency adaptive routing.

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Restoring grasping and movement for people with amputations and neurological impairment is imperative for retrieving independence. Prosthetic limbs, which are becoming widespread therapeutic solutions can significantly restore grasping and improve the quality of life of people with amputations or neurological disabilities. However, unlike living agents that combine different sensory inputs to perform a complex task accurately, most prostheses use uni-sensory input, offer limited degrees of freedom and need long patient training.
We investigate adaptive prosthetic limbs to restore grasping and sensation for persons with amputation and neurological impairments. In particular, we develop non-invasive technologies directly interfacing the environment with the residual arm or legs.

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In this project, we collaborate with Aizu Computer Science Laboratories, Inc. and Banpu Japan to develop an AI-enabled, blockchain-based electric vehicle integration system for power management in a smart grid platform based on EV and solar carport. We have developed a low-power AI-chip and various software tools for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy.

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