Welcome to the Intelligent Computing Lab at Yale University! 

We are located in the Electrical Engineering Department at Dunham Labs. The lab is led by Prof. Priyadarshini PandaPriya’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:

  1.  National Science Foundation

  2.  Technology Innovation Institute

  3. Center for Co-design of Cognitive Systems

  4. DARPA AIE on Shared Experience Lifelong Learning


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