Room B321, E-Quad
I am a Ph.D. candidate at the Department of Electrical and Computer Engineering, Princeton University under the supervision of Niraj K. Jha. My main research areas are Machine Learning, Neuroscience-inspired AI, Edge Computing, and Embedded systems. Prior to this, I was an undergraduate student at the Department of Electrical Engineering at the Indian Institute of Technology Delhi. I am also the founder and CEO of Qubit Inc., a company that works on providing next generation solutions for industrial problems. I have also worked as a visiting researcher at the Embedded Systems Laboratory (ESL), Institute of Electrical Engineering at EPFL, Switzerland.
My publications can be seen here. I have also reviewed for top journals including IEEE TCAD, TEVC, TETC, TII and Wiley SPE. I have also reviewed papers for reputed conferences like CISS, CogSci and ICML. View my CV here.
- BREATHE: Second-Order Gradients and Heteroscedastic Emulation based Design Space ExplorationarXiv Preprint 2023
- TransCODE: Co-design of Transformers and Accelerators for Efficient Training and InferenceIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2023
- EdgeTran: Co-designing Transformers for Efficient Inference on Mobile Edge PlatformsarXiv Preprint 2023
- AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with TransformersIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2023
- CODEBench: A Neural Architecture and Hardware Accelerator Co-Design FrameworkACM Transactions on Embedded Computing Systems 2022
- DINI: Data Imputation using Neural Inversion for Edge ApplicationsNature Scientific Reports 2022
- FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?Journal of Artificial Intelligence Research 2022
- Generative Optimization Networks for Memory Efficient Data GenerationNeurIPS 2021 - Workshop on ML for Systems 2021
- Are Convolutional Neural Networks or Transformers more like human vision?Annual Meeting of the Cognitive Science Society (CogSci) 2021
- AVAC: A Machine Learning based Adaptive RRAM Variability-Aware Controller for Edge DevicesIEEE International Symposium on Circuits and Systems (ISCAS) 2020
- RRAM-VAC: A variability-aware controller for RRAM-based memory architecturesAsia and South Pacific Design Automation Conference (ASPDAC) 2020
- Design of a Conventional-Transistor-Based Analog Integrated Circuit for On-Chip Learning in a Spiking Neural NetworkInternational Conference on Neuromorphic Systems (ICONS) 2020
- Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud ComputingInternet of Things 2020