Rasool Sharifi
Papers
1
Total Citations
10
H-Index
1
About
Rasool Sharifi is a researcher at the forefront of efficient deep learning deployment on edge devices, with a primary focus on hardware-software co-design for embedded systems. His most cited work, "Inf4Edge" (2021), addresses the critical challenge of deploying computationally intensive Convolutional Neural Networks (CNNs) on resource-constrained edge FPGAs. This paper introduces an automatic, resource-aware framework that generates energy-efficient CNN inference accelerators, enabling real-time computer vision on low-power hardware—a breakthrough for applications like autonomous drones and IoT devices. With 10 citations in a short period, this work demonstrates growing impact in the field of embedded AI. Sharifi's contributions lie at the intersection of machine learning, FPGA design, and energy optimization, offering practical solutions for bringing high-performance AI to the edge. His research is particularly valuable for students and engineers seeking to bridge the gap between powerful neural networks and the stringent power and area budgets of embedded platforms. By automating the generation of efficient accelerators, Sharifi is helping to democratize edge AI, making it accessible for a new generation of smart, autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1