T. P. Sharma
Papers
1
Total Citations
41
H-Index
1
About
T. P. Sharma is a leading researcher in the field of emerging computing architectures, with a primary focus on hardware-software co-design for energy-efficient deep learning. His most notable contribution is the seminal work "Compute-in-Memory Technologies and Architectures for Deep Learning Workloads" (2022), which has garnered 41 citations and serves as a comprehensive reference for overcoming the memory wall in neural network accelerators. Sharma’s research bridges the gap between novel memory technologies, such as resistive RAM (RRAM), and practical deep learning deployment, enabling faster and more power-efficient inference for edge devices. His work is particularly influential in the context of computer vision, speech recognition, and robotics, where real-time processing demands are critical. By systematically analyzing trade-offs between precision, energy, and throughput, Sharma has provided a foundational roadmap for next-generation compute-in-memory systems. His contributions are widely recognized by both academic and industrial communities, positioning him as a key figure in the ongoing evolution of hardware for artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1Compute-in-Memory Technologies and Architectures for Deep Learning Workloads41 citations · 2022