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About

Zichen Fan is a researcher specializing in low-power computer vision, edge AI hardware, and autonomous systems, with a particular focus on designing energy-efficient vision systems for resource-constrained platforms. His work bridges the gap between advanced machine learning algorithms and practical hardware implementation, addressing one of the most pressing challenges in modern computing: enabling sophisticated visual intelligence on power-limited devices. Fan's most influential contribution, "Low-Power Computer Vision: Status, Challenges, and Opportunities" (2019, 76 citations), established a foundational framework for understanding the landscape of efficient vision processing on mobile and autonomous platforms. Building on this, his hardware-focused research culminated in the development of RoboVisio and its predecessor SoC, a 22nm chip achieving an impressive 3.5 TOPS/W efficiency with 2MB embedded MRAM for fully on-chip neural network storage — eliminating costly off-chip memory access in micro-robotic applications. His most recent work, RoboVisio (2024), advances this vision further with a novel hybrid processing element capable of handling both CNN and classical vision tasks. Fan's research is particularly valuable for robotics and IoT communities, demonstrating that full autonomous navigation intelligence can be realized within the tight energy budgets of micro-robotic systems.