Arthur Siqueira
Papers
1
Total Citations
9
H-Index
1
About
Arthur Siqueira is a researcher at the forefront of embedded artificial intelligence, with a primary focus on the efficient execution of deep neural networks (DNNs) on resource-constrained edge devices and collaborative robots. His most-cited work, "Characterizing the Execution of Deep Neural Networks on Collaborative Robots and Edge Devices" (2019), addresses a critical bottleneck in modern robotics: how to process complex data locally without relying on cloud infrastructure. By systematically analyzing the performance trade-offs of DNN inference on low-power hardware, Siqueira provides foundational insights into achieving high-performance AI on the edge—enabling faster, more autonomous, and privacy-preserving robotic systems. This work has garnered 9 citations and is frequently referenced by researchers tackling real-time machine learning in robotics. Siqueira’s contributions are particularly notable for bridging the gap between theoretical deep learning and practical deployment, making him a key voice in the growing field of edge AI. His research continues to shape how collaborative robots perceive and interact with their environment, promising smarter, more responsive automation in manufacturing, healthcare, and beyond.
Research Focus
Key Achievements
Top Papers
- 1