Sign changes and resonance of intrinsic spin Hall effect in two-dimensional hole gas

The intrinsic spin Hall conductance shows rich sign changes by applying a perpendicular magnetic field in a two-dimensional hole gas. Especially, a notable sign changes can be achieved by adjusting the characteristic length of the Rashba coupling and hole density at moderate magnetic fields. This sign issue may be easily realized in experiments. The oscillations of the intrinsic spin Hall conductance as a function of 1/$B$ is nothing else but Shubnikov-de Haas oscillations, and the additional beatings can be quantitatively related to the value of the spin-orbit coupling parameter. The Zeeman splitting is too small to introduce effective degeneracy between different Landau levels in a two-dimensional hole gas, and the resonant intrinsic spin Hall conductance appears in high hole density and strong magnetic field due to the transition between mostly spin-$-{1/2}$ holes and spin-3/2 holes is confirmed. Two likely ways to establish intrinsic spin Hall effect in experiments and a possible application are suggested.Comment: Accepted for publication in Applied Physics Letters, an enlarged version, 5 pages, 4 fig

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