Featured Research Areas
We investigate machine learning (ML)-based methodologies for the design of advanced computing systems using emerging technologies to enable energy-efficient, high-performance, and reliable inference and training of emerging ML models. Our research focuses on cross-layer hardware and software co-design spanning across applications, architecture, and advanced packaging.
Specific topics include:
Design of advanced computing systems using emerging technologies (e.g., compute-in-memory, silicon photonics)
ML for design space exploration and dynamic resource management in computing systems
Design for reliability in advanced packaging (e.g., 2.5D chiplets and 3D integration)
Emerging Compute Substrates
EDA challenges in In-memory Compute to Quantum computing
Neuromorphic Computing
Ultra-low power computing at the edge using Spiking Neural Networks
Advanced Packaging
Tackling thermomechanical issues, power delivery network, etc in advanced packaging
Prospective Students
Update May 2026: PhD positions are filled now.
Undergrads at USF are welcome to email me (narang[at]usf[dot]edu) for collaborations starting Fall 2026. I am always looking for good students and collaboration opportunities.
I am looking for self-motivated students who are interested in research at the intersection of hardware design and machine learning and have the following skills/understanding.
Strong programming skills (Python, C, Verilog) and a solid foundation in Computer architecture, Digital circuits, Embedded systems, Machine learning
Familiarity in neural networks (spiking, graph, transformers), large-language models (LLMs) and beyond
Working knowledge of Reinforcement learning, Supervised learning, Agentic AI
Understanding of 3D/2.5D integration, non-von Neumann architectures such as compute-in-memory, systolic arrays, and silicon photonics
Willingness to engage in hands-on hardware design (Verilog, logic synthesis, etc.)
Please do not use AI to draft your emails or responses.
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