Our lab builds real systems that operate at the frontiers of computation and physics. We first design biologically-inspired, hardware-friendly algorithms, then we design and architect hardware that runs these algorithms very efficiently. In the end, we use these chips in specific domains such as robotics, biomedical-implants, and anywhere we might need to embed intelligence.
Siddharth Joshi is an Assistant Professor in the department of Computer Science and Engineering at the University of Notre Dame since 2018. Prior to that, he was Postdoctoral Fellow in the department of Bioengineering at University of California San Diego. He completed his PhD in 2017 in the Electrical and Computer Engineering department at UC-SD where he also completed his M.S. in 2012. He also completed a B. Tech from Dhirubhai Ambani Institute of Information and Communication Technology in India. His research focuses on the co‐design of custom, non‐Boolean and non‐von Neumann, hardware and algorithms to enable machine learning and adaptive signal processing in highly resource constrained environments.
Clemens Schaefer's research interests focus on brain-inspired computing. He especially enjoys applying findings from neuroscience to topics in machine learning. This ranges from using biologically plausible spiking dynamics on a single neuron level to stochasticity in synapsis to hyperdimensional computing. He is passionate about finding new ways for computers to learn more efficiently. In the future, his goal is to partake in the development of cutting-edge brain-inspired computing hardware and algorithms. During his undergrad at Catholic University Eichstätt-Ingolstadt and masters studies at UCL in Europe, he was a scholarship holder of the Friedrich Naumann Foundation for Freedom and the German Academic Exchange Service (DAAD).
Patrick Faley is a computer engineering undergraduate student at the University of Notre Dame. His research so far has revolved around alternate implementations of common neural network architectures, with the goal of creating models capable of being implemented directly into hardware. He has also worked on implementing various statistical techniques to gain a deeper understanding of the inner workings of a neural network. In the future, Patrick hopes to implement his networks in hardware to create robust, low-power computer vision systems.
Md Shahrul Islam is a Computer Science and Engineering PhD student at the University of Notre Dame. His research interest includes implementation of novel bio-inspired algorithm in resource constraint environments. His work focuses into designing efficient low power analog and mixed signal systems to implement machine learning and signal processing tasks. Before joining University of Notre Dame he completed his Masters in Electrical and Computer Engineering from Southern Illinois University at Carbondale.
Pooria Taheri is a PhD student in the department of Computer Science and Engineering at the University of Notre Dame. His research interest revolves around designing accelerators using emerging technologies for machine learning applications more specifically Spiking neural networks (SNN). Before joining the University of Notre Dame, he graduated from the National University of Iran, and his research was mostly focused on parallel computations for classification algorithms e.g. k-means and VLSI designs. He also worked as a junior researcher at IPM (Institute for Research in Fundamental Science).
Kshama is a PhD student in Computer Science and Engineering department at the University of Notre Dame. Her research interests includes designing and implementation of low-power integrated circuits for biomedical and bio-inspired systems. Before joining University of Notre Dame, she graduated from University of Wyoming with B.S in Electrical Engineering and from Boise State University with M.S in Electrical Engineering.
Yasmein is a PhD student in Computer Science and Engineering department at the University of Notre Dame. Her research interests includes design and implementation of Digital Neuromorphic Integrated Circuits. She graduated from University of Science and Technology at Zewail City with a B.Sc. in Nanotechnology and Nanoelectronics Engineering.
Thomas is a Ph.D. student in the Department of Computer Science and Engineering at the University of Notre Dame. His research interests include biologically inspired machine learning and edge AI applications. Thomas graduated from Brown University, concentrating in Computational Cognitive Neuroscience.
Jake Leporte is a Master's student in the department of Computer Science and Engineering at the University of Notre Dame. Jake also completed his undergradute studies at the University of Notre Dame, and commissioned into the US Air Force through ND's AFROTC Detachment 225. His research focuses on digital accelerator design for neuromorphic systems.
Samir is a Computer Science and Engineering PhD student at the University of Notre Dame. His research interests include applying analog computation towards Radio-Frequency applications. He is also interested into designing efficient low power analog and mixed signal systems to implement machine learning and signal processing tasks. Before joining University of Notre Dame he completed his Bachelors in Computer Science and Engineering from BRAC University at Dhaka, Bangladesh. After that he was in the industry, where he gained expertise in different FinFET technology nodes such as 3nm, 5nm, 16nm etc.
Our studies develop extremely power efficient processors and circuits that can operate under severe resource constraints such as sensors and processors used in the “Internet of Things” IOT. Our group explores hardware and software techniques to enable adaptive and learning algorithms and to be implemented in low-power architectures and circuits that use machine learning algorithms to operate at the limits of energy efficiency. The use of non-von Neumann architectures and both analog-CMOS and Beyond CMOS techniques enables these designs to operate at the extremes of energy efficiency.
Patrick Faley created a tool based on this paper. which allows users to analyze neural networks written in Pytorch to measure how well a network separates different classes during training. With this information, users can ensure that models are learning to distinguish between classes and determine which specific layers contribute the most to class separation. This can be used to make informed decisions about model utility and layer composition. The repository can be found here.
We are looking for motivated graduate students and postdoctoral scholars who are interested in the general area of Computer Architecture Circuit Design and Machine Learning.
We are actively looking for motivated students who are interested in pursuing Ph.D. degrees in the topic of exploring algorithms, architectures, or circuit-level techniques to develop energy-efficient intelligent systems.
At the Intelligent Microsystems Lab, our research traverses various levels of abstraction including systems, circuits, and algorithm design residing at the interface of machine intelligence and cyber-physical systems. We study neuromorphic and other non-von Neumann architectures where we leverage energy efficiencies in analog-CMOS and alternative (Beyond CMOS) computing structures to deliver orders of magnitude improvement in the performance of adaptive and learning systems. We work closely with groups developing new devices and materials to help us develop new chips aimed at achieving the limits of energy efficiency.
If you are a postdoc candidate, please send us an email containing your CV, research statement, and two names for requesting reference letters.