Satyaprakash Pareek
Papers
1
Total Citations
21
H-Index
1
About
Satyaprakash Pareek is a leading researcher in high-performance computer architecture and hardware acceleration, with a primary focus on convolutional neural network (CNN) accelerators. His most cited work, "XVDPU: A High Performance CNN Accelerator on the Versal Platform Powered by the AI Engine" (2022, 21 citations), addresses critical bottlenecks in modern deep learning—namely, the immense computation and I/O demands of larger, more accurate networks. Pareek’s major contribution lies in pioneering efficient accelerator designs on Xilinx’s cutting-edge 7nm Versal platform, leveraging its AI Engine to dramatically improve throughput and energy efficiency for computer vision tasks. This work is notable for tackling the real-world challenge of deploying high-resolution CNNs in resource-constrained environments, bridging the gap between algorithmic complexity and hardware feasibility. With 21 citations and growing, Pareek’s research is shaping the next generation of domain-specific architectures, offering practical solutions for autonomous systems, edge computing, and beyond. His innovative approach to hardware-software co-design continues to inspire students and researchers aiming to push the boundaries of AI acceleration.
Research Focus
Key Achievements
Top Papers
- 1