Job Description
7 days ago
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;
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;
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