Indranil Chakraborty
Papers
1
Total Citations
41
H-Index
1
About
Dr. Indranil Chakraborty is a leading researcher in the field of energy-efficient hardware design for machine learning, with a primary focus on in-memory computing (IMC) architectures. His most cited work, "Circuits and Architectures for In-Memory Computing-Based Machine Learning Accelerators" (2020, 41 citations), addresses the critical challenge of bridging the gap between the growing complexity of deep neural networks (DNNs) and the limitations of conventional von Neumann architectures. Dr. Chakraborty’s major contribution lies in developing novel circuit and system-level designs that enable matrix-vector multiplication—the core compute primitive of DNNs—to be performed directly within memory arrays. This approach dramatically reduces the energy and latency costs associated with data movement, which is a primary bottleneck in modern accelerators. His research has profound implications for deploying advanced AI in resource-constrained edge devices, from computer vision to robotics. By pioneering efficient IMC architectures, Dr. Chakraborty is helping to realize the next generation of low-power, high-performance machine learning hardware, making him a key figure in the evolution of AI acceleration.
Research Focus
Key Achievements
Top Papers
- 1