FACULTY

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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Driven by the advances in AI, computer architecture, and sensor technologies, automobiles, including electric vehicles (EVs) and self-driving cars, are transforming into sophisticated high-performance computing platforms. As the advancement of these computing systems accelerates, they will be running a wide variety of applications, including sensing, navigation, etc., using specialized deep neural network systems and complex communication protocols (i.e., Ethernet, SDVs) with safety and reliability support. We study advanced automotive computing systems, including AI-powered EV Energy Harvesting and Management, EV Campus Energy Trading, and AI-enabled automotive ICs.

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Brain-inspired (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.We research adaptive low-power spiking neuromorphic systems and SoCs empowered with our earlier developed fault-tolerant three-dimensional on-chip interconnect technology. In particular, we investigate innovative algorithms and neuromorphic systems, including adaptive configuration methods to enable the reconfiguration of different network parameters (spike weights, routing, hidden layers, topology, etc.), fault-tolerance, thermal-aware mapping methods, and on-line learning algorithms. The target applications include anthropomorphic robotics and edge computing.


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Our research in power and energy-efficient computing systems is essential to meeting the growing demand for more powerful and sustainable technology. As society increasingly relies on computing devices, managing their energy consumption becomes crucial. By developing efficient computing systems, we can significantly reduce energy costs, minimize environmental impact, and extend the battery life of portable devices. In large-scale data centers, enhancing energy efficiency leads to substantial cost savings and a reduced carbon footprint. Our work in this field fosters innovation in hardware and software design, paving the way for smarter, greener technologies that benefit both users and the planet.

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