Hadi Zamani
Papers
1
Total Citations
10
H-Index
1
About
Hadi Zamani is a researcher at the forefront of efficient deep learning deployment on edge devices, with a primary focus on FPGA-based acceleration of convolutional neural networks (CNNs). His most-cited work, "Inf4Edge" (2021), introduces a groundbreaking framework for automatically generating resource-aware, energy-efficient CNN inference accelerators tailored for edge-embedded FPGAs. This contribution directly addresses the critical challenge of deploying computationally intensive CNN models on resource-constrained hardware, enabling real-time computer vision in applications like autonomous drones and IoT sensors. By optimizing the trade-off between accuracy, power consumption, and hardware resources, Zamani’s research bridges the gap between high-performance deep learning and practical edge deployment. With 10 citations on this flagship paper, his work is gaining traction among researchers and engineers seeking to democratize AI on low-power platforms. Zamani’s achievements highlight his expertise in hardware-software co-design, making him a key figure in the push toward sustainable, on-device intelligence.
Research Focus
Key Achievements
Top Papers
- 1