Research Highlights
Performance Characterization of NVIDIA's Tegra Orin SoC (Research in progress)
In collaboration with Dr. Jenny Huang, Research Scientist at NVIDIA, I am conducting a performance
characterization of the Tegra Orin SoC, focusing on its efficiency with intensive multimodal vision-language models.
We aim to understand critical bottlenecks in the system for such workloads, including memory bandwidth limitations, computational latency, and thermal constraints.
This reseach is also a preparation for my PhD research aspirations in novel microarchitecture for accelerated inference on edge systems.
Content Addresseable Memory (Physical Design with 3nm TSMC Technology)
This study implements a 64bit Content addressable memory(CAM) in a 3nm technology with the goal of creating a design
optimized for minimum energy delay and area product. A 10-T NOR CAM bitcell architecture was chosen for
its fast bit-matching as well as bigger noise margin. Search-line conditioning was eliminated to reduce energy consumption,
additionally, the write-enable signal was used to disable SLs during writing. Minimum sized transistors were used to optimize for area
and energy. Implimented using Synopsys Design Compiler, the design was optimized for minimum EDA product with successful post-layout simulations.
Physics-informed neural network for inverse heat conduction estimation in electronic packages
The novelity of this study lies in the ability to perform thermal simulation studies solely based on
microarchitectural performance and power simulations, an area that lacks support during pre-silicon design
phase. Additionally, engineers can use the provided inverse conduction modeling approach to create and
analyse chip surface heat maps while performing physical chip thermal characterization. The latter comes useful in hotspot identification and during
troubleshooting of various cooling solutions, including interface materials, heatspreaders and vapour
chambers, without having the need to invest in expensive IR cameras and complex setups for surface die heat maps.
Machine Learning assisted building energy modeling (Surrogate modeling for physics models)
A research study as part of masters thesis at Florida Tech; we focused on building a machine learning model for ML-assisted Building Energy Models (BEMs).
We developed an efficient approach to design space exploration for training the model by creating a Python-based automation program
that used EnergyPlus solvers and APIs to iteratively generate various physical models within a search space.