1. AI Operator Adaptation Development: Implement custom operator development for frameworks like NVIDIA and Horizon, or perform equivalent refactoring for incompatible operators; 2. Develop Heterogeneous Computing Pipelines: Achieve coordinated inference across devices including CPUs, GPUs, DLAs, and NPUs; 3. Performance Bottleneck Analysis: Establish a three-dimensional evaluation model for compute power, latency, and power consumption to identify computational bottlenecks; 4. Model lightweighting: Optimize deep learning models using techniques like quantization, pruning, sparsification, and structural replacement to reduce computational demands and power consumption. Develop generic frameworks for rapid adaptation to diverse task models; 5. Pre/post-processing optimization: Enhance code efficiency through CUDA/OpenCL implementation; 1. AI算子適配開發：實現NVIDIA、地平線等框架的Custom OP開發或實現不相容算子的等效重構； 2. 開發異構計算流水線：實現CPU、GPU、DLA、NPU等設備的協調推理； 3. 性能瓶頸分析：建立算力-時延-功耗三維評估模型，識別計算瓶頸； 4. 模型輕量化改造：利用量化、剪枝、稀疏、結構替換等模型優化方法，對深度學習模型進行輕量化處理，降低算力需求與功耗。整理通用框架，實現對各任務模型的快速適配； 5. 前後處理優化：利用CUDA/OpenCL實現對代碼效率的提升；

1. Master the theoretical foundations and algorithms of artificial intelligence, pattern recognition, deep learning, and computer vision; 2. Understand typical model distillation, pruning, and quantization techniques; candidates with practical experience in robotics projects or competition experience are preferred; 3. Proficient in C/C++ and Python programming, familiar with Linux systems and ROS systems; 4. Possess strong teamwork and communication skills, along with excellent learning capabilities. 1. 掌握人工智慧、模式識別、深度學習、電腦視覺理論基礎和演算法； 2. 瞭解典型的模型蒸餾、剪枝、量化技術，有機器人專案實際經驗或比賽經驗者優先； 3. 熟悉C/C++和Python編程，熟悉Linux系統和ROS系統； 4. 具備良好的團隊協作能力和溝通能力，具備良好的學習能力。