NOVOSENSE Company successfully achieved localized deployment of DeepSeek, and the technical white paper details implementation paths and innovation value.
Project background and technical architecture
Industry-driven and strategic positioning
As a leading domestic chip design company, NOVOSENSE has profound technical accumulation in the fields of smart sensors, automotive electronics, etc. With the in-depth application of AI technology in industrial scenarios, traditional cloud AI solutions face the pain points of insufficient data privacy, network latency and customization. As a benchmark in the field of open source big models, DeepSeek’s localized deployment capabilities provide NOVOSENSE with an opportunity to build an “end-side intelligence + edge computing” ecosystem.
Technology integration architecture
This deployment adopts the dual-engine driver architecture of “domestic chip + DeepSeek engine”:
- Hardware layer: Select Asent 910B AI processor (supports FP16/INT8 hybrid accuracy calculation), integrates NOVOSENSE self-developed NPU acceleration module, and achieves a 3.2-fold increase in model inference energy efficiency.
- Model Layer: Deploy DeepSeek-V3 multimodal model (parameter scale 671 billion) and compress it to 1/8th accuracy loss <0.3% through knowledge distillation technology
- System Layer: Build an AI inference framework based on OpenHarmony operating system, supporting dynamic voltage frequency regulation (DVFS) technology to reduce power consumption by 30%
Safety Compliance Design
- Data encryption: SM4-GCM algorithm is used to encrypt the transmitted data 256-bit, supporting the national secret SSL protocol
- Access Control: Implement the separation of powers mechanism (data collection rights, model training rights, and inference service rights separation)
- Audit Track: Build a blockchain operation log to meet GDPR, HIPAA and other compliance requirements
Implement deployment full-link analysis
Hardware Adaptation and Optimization
| Components | Model/parameters | Optimization strategy |
| Computing unit | Ascend 910B*8 card cluster | NVLink3.0 high-speed interconnection, AllReduce communication efficiency is 95% |
| Storage system | All-flash array (NVMe SSD) | Deploy Ceph distributed storage, with IOPS reaching 1.5 million |
| Network architecture | 25G RoCEv2 Network | Using Spine-Leaf topology, end-to-end delay <3μs |
| Power system | 2N redundant platinum power supply | Support dynamic power capping technology |
Software deployment process
- Environment preparation: Install Kylin OS V10 (adapted to Kunpeng architecture), configure CUDA 12.1+cuDNN 8.9 environment
- Model conversion: Use ATC tool to convert PyTorch model to OM format, implement graph optimization (operator fusion, constant folding)
- Service orchestration: Deploy DeepSeek service through Kubernetes, set GPU sharing scheduling strategy (binpack algorithm)
- Monitoring docking: Integrate Prometheus+Granfana monitoring stack, configure key indicator alarm rules:
- GPU utilization>85% (trigger alarm for 5 minutes)
- Model response time P99>200ms (trigger automatic expansion and contraction)
Performance tuning practice
- Quantization compression: Use QAT quantization-aware training + TensorRT optimization to achieve 4.1 times FP16 reasoning acceleration
- Cache strategy: Deploy Redis Cluster caches high-frequency requests, and the hit rate is increased to 78%
- Load balancing: Implement a dynamic weight allocation algorithm based on GPU memory to reduce cross-node data transmission by 32%
Industry application scenarios and results
Intelligent sensor data analysis
- Scenario: Predictive maintenance of industrial equipment
- Solution: Deploy a vibration signal analysis model to process 12-channel sensor data in real time
- Result: Fault prediction accuracy is 92.3% (18% higher than traditional algorithms)
Automotive electronics test verification
- Scenario: ADAS system simulation test
- Solution: Build a multimodal test scenario library (including 4K video stream + radar point cloud data)
- Result: The test cycle is shortened by 65%, and the scenario coverage rate is increased to 99.7%
Intelligent manufacturing production line optimization
- Scenario: Semiconductor wafer inspection
- Solution: Develop a defect classification model (supporting 23 defect type identification)
- Results: Detection throughput reaches 480 pieces/hour (12 times higher than manual detection)
Business value and technology outlook
Economic benefit analysis
- Hardware cost: Domestic solutions are 40% lower than similar imported solutions
- Operation and maintenance cost: Automated operation and maintenance platform reduces manpower investment by 60%
- Potential benefits: It is expected to drive the shipment volume of smart sensors to grow by 150% within three years
Technology evolution route
- Short-term (1-2 years): Realize the localization of the entire process of model training-reasoning
- Medium-term (3-5 years): Develop an end-cloud collaborative reasoning framework to support 5G edge computing scenarios
- Long-term (more than 5 years): Build an independent AI chip ecosystem, and increase the energy efficiency ratio of the target model by 10 times
Ecological layout planning
- Establish DeepSeek+ domestic hardware developer community
- Build industry knowledge graphs with leading car companies and industrial equipment manufacturers
- Exploring the application of federated learning in supply chain quality management
This white paper elaborates on the technical implementation path, industry application results and future development plan of NOVOSENSE’s DeepSeek localized deployment. Through in-depth technical analysis and business insights, it provides a replicable paradigm for the implementation of AI in the field of intelligent manufacturing.
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