Adewale Adetomi
Papers
2
Total Citations
30
H-Index
2
About
Adewale Adetomi is an emerging researcher specializing in edge artificial intelligence, hardware accelerator design, and energy-efficient computing systems. His work sits at the critical intersection of machine learning and embedded hardware, addressing one of the most pressing challenges in modern computing: deploying powerful AI capabilities within severely constrained power and size budgets. Adetomi's most recognized contribution examines low-power, ultra-small edge AI accelerators designed for convolutional neural network-based image recognition, a paper that has collectively garnered approximately 30 citations across its publications. This work provides a rigorous analysis of accelerator architectures suited for demanding real-world applications, including unmanned aerial vehicles, wearable devices, robotics, and remote sensing satellites — environments where both performance resilience and energy efficiency are non-negotiable requirements. By synthesizing current design trends and charting future directions, his research serves as a valuable reference for engineers and researchers building the next generation of intelligent edge devices. His scholarship speaks directly to the growing demand for AI at the network edge, where cloud connectivity is limited or impractical. For students and researchers exploring embedded AI, hardware-software co-design, or IoT intelligence, Adetomi's analytical frameworks offer a strong foundational perspective on where the field is headed.
Research Focus
Key Achievements
Top Papers
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- 2