PI-BA Bundle Adjustment Acceleration on Embedded FPGAs with Co-observation Optimization

Bundle adjustment (BA) is a fundamental optimization technique used in many crucial applications, including 3D scene reconstruction, robotic localization, camera calibration, autonomous driving, space exploration, street view map generation etc. Essentially, BA is a joint non-linear optimization problem, and one which can consume a significant amount of time and power, especially for large optimization problems. Previous approaches of optimizing BA performance heavily rely on parallel processing or distributed computing, which trade higher power consumption for higher performance. In this paper we propose {\pi}-BA, the first hardware-software co-designed BA engine on an embedded FPGA-SoC that exploits custom hardware for higher performance and power efficiency. Specifically, based on our key observation that not all points appear on all images in a BA problem, we designed and implemented a Co-Observation Optimization technique to accelerate BA operations with optimized usage of memory and computation resources. Experimental results confirm that {\pi}-BA outperforms the existing software implementations in terms of performance and power consumption.Comment: in Proceedings of IEEE FCCM 201

A Comprehensive Review and Systematic Analysis of Artificial Intelligence Regulation Policies

Due to the cultural and governance differences of countries around the world, there currently exists a wide spectrum of AI regulation policy proposals that have created a chaos in the global AI regulatory space. Properly regulating AI technologies is extremely challenging, as it requires a delicate balance between legal restrictions and technological developments. In this article, we first present a comprehensive review of AI regulation proposals from different geographical locations and cultural backgrounds. Then, drawing from historical lessons, we develop a framework to facilitate a thorough analysis of AI regulation proposals. Finally, we perform a systematic analysis of these AI regulation proposals to understand how each proposal may fail. This study, containing historical lessons and analysis methods, aims to help governing bodies untangling the AI regulatory chaos through a divide-and-conquer manner