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 Panda. Priya’s research interests lie in Neuromorphic Computing, spanning developing novel algorithms and architectures (with CMOS/emerging technology) for both deep learning and more brain-inspired spiking neural networks. Our group is also a part of the Computer Systems Lab at Yale.
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 appropriateness of intelligent systems, like robustness, explainability, security in adversarial scenarios, adaptivity or lifelong learning in a real-time complex environment and also, 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-appropriateness tradeoff cohesively with algorithm-hardware co-design to create truly functional intelligent systems. We are also interested in exploring bio-plausible algorithms-and-hardware guided by natural intelligence (how the brain learns, the internal fabric of the brain etc.) to define the next generation of robust and efficient AI systems for beyond-vision static recognition tasks with the ability to perceive, reason, decide autonomously in real-time.