Parsa Research Laboratory

Research Areas and Interests

At Parsa Research Laboratory (PRL) we are focused on developing full stack novel brain-inspired paradigms that enable accurate, energy efficient, fast, and reliable intelligence at the edge. These span from algorithm-hardware codesign to physics-informed neuromorphic computing, distributed learning, and safe lifelong learning.

PRL Research Topics
Sponsored Projects

Title: Learning Neuromorphic Physics-Informed Stochastic Regions of Attraction through Bayesian Optimization (single-PI)
Sponsor: Intel Neuromorphic Research Community (INRC)
Collaborator: Dr. Joe Hays, Naval Research Laboratory
Goal: The ultimate goal of this project is to enable agents, who are learning to control themselves, to have an inherent sense of their own stability, and thus enable them to learn and expand their capabilities safely. This work aims to use Bayesian optimization (BO) to learn a distribution of Lyapunov regions of attraction (ROA). ROA defines the boundary between stability and instability. Once learned offline, the policy will be able to discriminate which specific parameter set defines the specific system under control from the state/action data and therefore refine the ROA for the specific agent.
Impact: Endowing agents with a sense of their own stability through an estimate of their own ROA (pulled from an offline characterized stochastic ROA distribution) is a strong step forward in our community’s pursuit of having safe lifelong motor learning. The ROA will enable the agent to maximize the exploration needed for learning while remaining safe.


News & Updates
  • Dr. Parsa invited to join Institute for Digital Innovation (IDIA) 2022
  • Dr. Parsa invited to join “Center for Trusted, Accelerated, and Secure Computing and Communication (C-TASC)” 2022
  • PRL was awarded a three-year fund from Intel Corporation (INRC) for “Learning Neuromorphic Physics-Informed Stochastic Regions of Attraction through Bayesian Optimization” 2021
  • Dr. Parsa received best paper award for "Accurate and Accelerated Neuromorphic Network Design Leveraging a Bayesian Hyperparameter Pareto Optimization Approach", International Conference on Neuromorphic Computing (ICONS) 2021
  • Dr. Parsa received best paper award for "Avoiding Excess Computation in Asynchronous Evolutionary Algorithms", UK Workshop on Computational Intelligence (UKCI) 2021
  • Dr. Parsa invited to serve on the program committee at the TinyML Research Symposium 2021