This paper presents ERSRP, a 55nm Edge Real-Time Super-Resolution Processor that achieves 104 FPS at FHD resolution with a peak area efficiency of 46.28 MPixels/(s·mm2), 8.86× higher than the state-of-the-art. The processor is designed through a software-hardware co-optimization approach, addressing the low utilization, high memory demand, and workload imbalance challenges inherent in lightweight SR networks. At the algorithmic level, an Ultra-Lightweight Super-Resolution (ULSR) model is proposed that integrates depth-wise and point-wise separable blocks with a pixel-shuffle mechanism to achieve high-quality reconstruction (37.18 dB PSNR and 0.9581 SSIM on Set5) with only 5.62K parameters. At the hardware level, the ERSRP introduces a Lightweight Accelerated Engine (LAE) sup-porting a Layer Parallel Computing Scheme (LPCS) to improve lightweight operator throughput by 48.9%. A Point-wise Layer Fused Scheme (PLFS) further enhances utilization by 4.95× through inter-core and intra-core fusion without intermediate memory. Fabricated in a 55nm UMC CMOS process, the ERSRP achieves a throughput of 215.7 MPixels/s and supports 104 FPS real-time SR at FHD.

Computing-in-memory (CIM) architecture has become a promising solution to improve the parallelism of the multiply-and-accumulation (MAC) operation for artificial intelligence (AI) processors. Recently, revived CIM engine partly relieves the memory wall issue by integrating computation in/with the memory. However, current CIM solutions still require large data movements with the increase of the practical neural network model and massive input data. Previous CIM works only considered computation without concern for the memory attribute, leading to a low memory computing ratio. This article presents a static-random access-memory (SRAM)-based digital CIM macro supporting pipeline mode and computation-memory-aware technique to improve the memory computing ratio. We develop a novel weight driver with fine-grained ping-pong operation, avoiding the computation stall caused by weight update. Based on our evaluation, the peak energy efficiency is 19.78 TOPS/W at the 22-nm technology node, 8-bit width, and 50% sparsity of the input feature map.

The reasonable trajectory prediction of surrounding traf-fic participants is crucial for autonomous driving. Espe-cially, how to predict multiple plausible trajectories is still a challenging problem because of the multiple possibilities of the future. Proposal-based prediction methods address the multi-modality issues with a two-stage approach, com-monly using intention classification followed by motion re-gression. This paper proposes a two-stage proposal-based motion forecasting method that exploits the sliced lane seg-ments as fine-grained, shareable, and interpretable propos-als. We use Graph neural network and Transformer to en-code the shape and interaction information among the map sub-graphs and the agents sub-graphs. In addition, we propose a variance-based non-maximum suppression strategy to select representative trajectories that ensure the diversity of the final output. Experiments on the Argoverse dataset show that the proposed method outperforms state-of-the-art methods, and the lane segments-based proposals as well as the variance-based non-maximum suppression strategy both contribute to the performance improvement. More-over, we demonstrate that the proposed method can achieve reliable performance with a lower collision rate and fewer off-road scenarios in the closed-loop simulation.

In this paper, we present a novel learnable and continuous Monte-Carlo Tree Search method, named as KB-Tree, for motion planning in autonomous driving. The proposed method utilizes an asymptotical PUCB based on Kernel Regression (KR-AUCB) as a novel UCB variant, to improve the exploitation and exploration performance. In addition, we further optimize the sampling in continuous space by adapting Bayesian Optimization (BO) in the selection process of MCTS. Moreover, we use a customized Graph Neural Network (GNN) as our feature extractor to improve the learning performance. To the best of our knowledge, we are the first to apply the continuous MCTS method in autonomous driving. To validate our method, we conduct extensive experiments under several weakly and strongly interactive scenarios. The results show that our proposed method performs well in all tasks, and outperforms the learning-based continuous MCTS method and the state-of-the-art Reinforcement Learning (RL) baseline.

Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solution. Due to the coupled action space of behavior and motion, it is challenging to solve HBMP problem using reinforcement learning (RL) for long-horizon driving tasks. We transform HBMP problem by integrating a classical sampling-based motion planner, of which the optimal cost is regarded as the rewards for high-level behavior learning. As a result, this formulation reduces action space and diversifies the rewards without losing the optimality of HBMP. In addition, we propose a sharable representation for input sensory data across simulation platforms and real-world environment, so that models trained in a fast event-based simulator, SUMO, can be used to initialize and accelerate the RL training in a dynamics based simulator, CARLA. Experimental results demonstrate the effectiveness of the method. Besides, the model is successfully transferred to the real-world, validating the generalization capability.