Abstract: Artificial Intelligence (AI) is poised to revolutionize society, yet its escalating energy demands pose a formidable challenge to its long-term sustainability. The staggering gap in energy consumption between biological (Human Brain @20watts) and artificial intelligence (ChatGPT @100KWatts) is striking. My research aims to bridge this gap with a bio-inspired, integrative approach, where algorithm-hardware co-design and neuromorphic computing converge to create intelligent, energy-efficient systems. In this talk, I will talk about my group’s recent efforts towards enabling and democratizing spike-based machine intelligence design, simulation, and evaluation across different applications. I’ll explore the distinctive benefits of Spiking Neural Networks (SNNs), especially the use of temporal dynamics, which enhances robustness while offering significant gains in latency, energy efficiency, and accuracy. From a hardware perspective, I’ll examine how memory and sparsity management can accelerate SNNs on general-purpose platforms, introducing techniques like input-aware dynamic temporal exit and scaling-free quantization for efficient weight and activation compression. Finally, I will share our ongoing efforts in input-aware adaptive computation and optimization-assisted model-hardware design space exploration that hold promise for developing energy-efficient and agile systems with less effort.

Bio: Priya Panda is an assistant professor in the Electrical & Computer Engineering department at Yale University, USA and a Visiting Faculty Researcher at Google DeepMind with the vision and compilers/architectures team. She received her B.E. and Master's degree from BITS, Pilani, India in 2013 and her Ph.D. from Purdue University, USA in 2019. During her PhD, she interned in Intel Labs where she developed large scale spiking neural network algorithms for benchmarking the Loihi chip. She is the recipient of the 2019 Amazon Research Award, 2022 Google Research Scholar Award, 2022 DARPA Riser Award, 2023 NSF CAREER Award, 2023 DARPA Young Faculty Award, and the inaugural 2024 Purdue Engineering 38 under 38 award. She has also received the 2022 ISLPED Best Paper Award, 2022 IEEE Brain Community Best Paper Award and 2024 ASP-DAC Best Paper Nomination. Her research interests lie in Spiking Neural Networks, Efficient AI algorithm and hardware design.