Welcome to the Intelligent Computing Lab at Yale University!
We are located in the Electrical & Computer Engineering Department at Dunham Labs. The lab is led by Prof. Priyadarshini Panda. Priya’s research interests lie in Neuromorphic Computing, spanning developing novel algorithms, architectures, systems, circuits (with CMOS/emerging compute-in-memory technology) and co-optimization techniques for deep learning and brain-inspired spiking neural networks for enabling on-device intelligence.
Our Focus and Approach:
‘Can machines think?’ the question brought up Alan Turing, presents several opportunities for research across the computing stack from Algorithm-to-Hardware (includes, Architecture-Circuit-Device), that we would like to explore to enable energy-efficient and appropriate machine intelligence. Today, artificial intelligence or AI is broadly pursued by Deep learning and Neuromorphic Computing researchers in the design space of energy-accuracy tradeoff with the motif of creating a machine exhibiting brain-like cognitive ability with brain-like efficiency. However, there are several questions regarding, what we term as robustness of intelligent systems, like security in adversarial scenarios, adaptivity or lifelong learning in a real-time complex environment, on-device training/inference challenges with extreme resource constraints and, compatibility with hardware stack among others, that still remain unanswered and under-explored. With the advent of Internet of Things (IoT) and the necessity to embed intelligence in all technology that surrounds us, our research plan is to explore the energy-accuracy-robustness tradeoff cohesively with neuromorphic algorithm-hardware co-design to create truly functional intelligent systems.
Research Sponsors:
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National Science Foundation
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Technology Innovation Institute
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Center for Co-design of Cognitive Systems
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DARPA AIE on Shared Experience Lifelong Learning
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DOE MMICC Center - SEA-CROGS
