System modeling and simulation are widely used in robotic vehicle engineering to reduce development time, improve the vehicle design, and miniaturize complex systems. This paper focuses on the steering control of a four-wheel steering vehicle. Optimally-tuned PID controllers are designed for autonomous ground vehicles. The required mission path and stability of steering are achieved by reducing the error between the vehicle heading and the target point. Predefined waypoints are set for the trajectory tracking system. An optimal genetic algorithm (GA) is proposed to tune the vehicle controllers. The proposed GA-tuned PID controller exhibits satisfactory simulation results and promising experimental results. This paper reports the current status of an ongoing research project for a fully autonomous unmanned ground vehicle (UGV).
Recent GPUs are equipped with general-purpose L1 and L2 caches in an attempt to reduce memory bandwidth demand and improve the performance of some irregular GPGPU applications. However, due to the massive multithreading, GPGPU caches suffer from severe resource contention and low data-sharing which may degrade the performance instead.In this work, we propose three techniques to efficiently utilize and improve the performance of GPGPU caches. The first technique aims to dynamically detect and bypass memory accesses that show streaming behavior. In the second technique, we propose dynamic warp throttling via cores sampling (DWT-CS) to alleviate cache thrashing by throttling the number of active warps per core. DWT-CS monitors the MPKI at L1, when it exceeds a specific threshold, all GPU cores are sampled with different number of active warps to find the optimal number of warps that mitigates thrashing and achieves the highest performance. Our proposed third technique addresses the problem of GPU cache associativity since many GPGPU applications suffer from severe associativity stalls and conflict misses. Prior work proposed cache bypassing on associativity stalls. In this work, instead of bypassing, we employ a better cache indexing function, Pseudo Random Interleaving Cache (PRIC), that is based on polynomial modulus mapping, in order to fairly and evenly distribute memory accesses over cache sets.The proposed techniques improve the average performance of streaming and contention applications by 1.2X and 2.3X respectively. Compared to prior work, it achieves 1.7X and 1.5X performance improvement over Cache-Conscious Wavefront Scheduler and Memory Request Prioritization Buffer respectively.
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