【事業内容】
◎通信事業者向けネットワーク事業
◎法人向けICTソリューション事業
◎コンシューマー向け端末事業
【当社について】
■ ファーウェイ (中国語表記:華為技術、英語表記:HUAWEI)は、1987年に中国・深センに設立された従業員持株制による民間企業であり、世界有数のICTソリューション・プロバイダーです。20万人以上の従業員の献身とお客様志向のイノベーション、お客様との強い信頼関係により、 通信事業者向けネットワーク事業 ・ 人向けICTソリューション事業 ・ コンシューマー向け端末事業 の各分野におけるエンド・ツー・エンドの競争優位性を確立しています。
ファーウェイは通信事業者、企業、消費者の皆様に最大の価値をもたらすべく、競争力の高い製品やサービスを170か国以上で提供し、世界人口の3分の1にもおよぶ人々のICTソリューション・ニーズに応えています。
■ ファーウェイ・ジャパン (華為技術日本株式会社)は2005年に設立。日本はファーウェイにとって重要な調達市場でもあり、日本研究所内のソーシング・センターでは優れた技術を持つ日本のサプライヤーとの協業関係を積極的に構築しています。
■ファーウェイのビジョンは、「通信技術を通じて人々の生活を豊かにする」ことです。通信業界で長年培ってきた経験やノウハウを生かし、情報格差の解消に努め、情報化社会がもたらす利益を誰もが享受できるよう尽力しています。
Research Subject
1. Efficient Implementation and Performance Optimization of Camera Low-Level Algorithms
- Main Content: This involves the implementation and optimization of core camera low-level algorithms such as image stabilization (IS), autofocus, image noise reduction, HDR fusion, etc. The focus is on C/C++ engineering implementation, with algorithm lightweighting and parallelization optimization tailored to embedded hardware characteristics (ISP, SoC).
- Technical Points:
- Efficient Coding: Writing efficient low-level code using C/C++ to optimize algorithm performance.
- Algorithm Performance Tuning: Optimizing the algorithm's computational efficiency, reducing latency, and improving its performance on embedded platforms (such as ISP, SoC).
- Embedded Systems: Understanding and deeply optimizing camera hardware (ISP, SoC) to resolve computational bottlenecks and latency issues.
2. Engineering Implementation of Camera Data Processing and Computational Resource Scheduling
- Main Content: This involves the processing and synchronization of multi-dimensional data from the camera (e.g., Gyro data, RAW data, ISP data), and designing a highly reliable camera data link and caching strategy. Additionally, the design of dynamic computational resource scheduling algorithms is needed to ensure a balanced resource allocation across various tasks (e.g., imaging, video recording, AI scene recognition) to ensure smooth camera operation in complex scenarios.
- Technical Points:
- Multi-dimensional Data Collection and Synchronization:: Ensuring efficient synchronization of data from multiple sensors (e.g., gyroscope, RAW image sensor) and performing effective preprocessing.
- Computational Resource Scheduling:: Designing and implementing dynamic resource scheduling algorithms to ensure a balanced and efficient use of resources across tasks (e.g., imaging, video recording, AI functions).
- Data Link and Caching Strategy: Designing reliable data transmission links and cache management mechanisms to ensure real-time performance and stability.
3. Algorithm Prototype Validation and Engineering Iteration of Camera AI Features
- Main Content: This involves algorithm prototype validation and logic implementation for camera AI features (e.g., scene recognition, image enhancement). The focus is on transitioning from algorithm research to engineering implementation, particularly in optimizing AI model preprocessing and postprocessing, as well as addressing AI model compatibility with camera hardware.
- Technical Points:
- Rapid Modeling and Prototype Validation: Quickly implementing AI algorithm prototypes to validate and iterate based on requirements.
- AI and Hardware Adaptation: Optimizing AI model preprocessing and postprocessing to ensure compatibility with camera hardware, improving inference speed and accuracy.
- Productization Iteration: Accelerating the transition from algorithm research to product functionality, facilitating the productization of AI features.
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