Pradip Sasmal

Indian Institute of Technology Hyderabad

Papers

Total Citations

H-Index

About

Pradip Sasmal is a researcher focused on the intersection of artificial intelligence and resource-constrained computing, with key contributions in deep neural network (DNN) optimization. His work addresses the critical challenge of deploying computationally and memory-intensive DNNs—essential for applications like robotics and autonomous vehicles—on platforms with limited resources. His most cited paper, "Clustered Network Adaptation Methodology for the Resource Constrained Platform" (2022), proposes a novel approach to adapt complex neural networks for efficient execution without sacrificing accuracy, garnering 3 citations. This methodology is pivotal for enabling AI in edge devices, where power and memory are scarce. Sasmal’s research bridges the gap between state-of-the-art AI performance and practical deployment, making him a notable contributor to the field of efficient machine learning. His work holds promise for advancing real-world AI systems, from self-driving cars to portable robotics, by ensuring that high-accuracy models can run effectively on constrained hardware.