Meng‐Fan Chang
Papers
2
Total Citations
17
H-Index
2
About
Meng‐Fan Chang is a leading researcher in energy-efficient edge computing and embedded memory technologies, with a focus on enabling intelligent, miniaturized robotic systems. His key research areas include resistive random-access memory (RRAM), neural network accelerators, and low-power VLSI design for autonomous surveillance platforms. Chang’s major contributions center on developing a 40nm VLIW edge accelerator that integrates 5 MB of embedded RRAM with an ultra-low 0.256 pJ/bit energy consumption, paired with a localization solver for bristle robot surveillance. This work addresses the critical challenge of balancing compute performance with extreme energy and size constraints in tiny robots, enabling real-time neural network inference and state-space equation solving for autonomous navigation. His most-cited paper (15 citations) demonstrates the accelerator’s ability to handle both perception and localization workloads, a breakthrough for miniaturized robotics. Chang’s innovations in embedded RRAM and edge AI have significant implications for low-power, compact autonomous systems, positioning him as a key figure in advancing the frontier of energy-efficient, intelligent edge devices.
Research Focus
Key Achievements
Top Papers
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- 2