The focus of the GREEN lab is to create scientific understanding and engineering techniques to design future generation computers. We seek to develop the smart machines that are intelligent to generate usable information from data, secure to ensure privacy and prevent malicious attacks, and finally, energy-effiienct to operate under tight resource constraints. We are breaking the traditional boundaries of design abstractions and creating a holistic understanding and methodology for integrated system design.
The GREEN lab is led by Prof. Saibal Mukhopadhyay. Our research interests include artificial intelligence, energy-efficient design, and secure hardware. We explore cross-layer innovations spanning algorithm, architecture, and mixed-signal circuit design.
Our DAC 2021 paper will present a methodology to improve the trustworthiness of hardware based malware detector using online uncertainty estimation.
Our recent paper at IEEE Transactions on Industrial Electronics (TIE) have demonstrated a low power authentication IC for visible light based interrogation to improve supply chain security.
Mandovi Mukherjee, Burhan Mudassar, and Minah Lee have received Best Student Paper Award (Third Place) in 2020 IEEE Sensors Conference.
We will present two invited/special session papers at Design, Automation, and Test in Europe (DATE). One paper will discuss the concept of closed-loop perception and second one will discuss the notion of reliable intelligence in unreliable environment.
We will present a paper at IEEE Transactions in Robotics and Automation Letter (T-RAL) and International Conference on Robotics and Automation (ICRA) on a machine learning based approach to control an unknown non-linear system.
In a recent paper published in Frontiers in Neuroscience, we have presented a new approach to design spiking neural network for unsupervised learning of spatiotemporal pattern using networks of neurons with different dynamics.