1. Development Context and Core Requirements

The rapid evolution of AI technologies has raised stringent demands on model training and inference environments in academic research. Traditional platforms face three major challenges: supply chain risks from imported GPUs, inadequate data security protection throughout its lifecycle, and data leakage vulnerabilities during multi-team collaboration. A key research laboratory’s innovation center developed this localized platform as a systematic solution addressing these pain points.

The platform adheres to three core principles: full-stack localization technology, end-to-end security architecture, and research scenario-specific customization. Through deep integration across hardware, operating system, and algorithm framework layers, it achieves complete control over the entire workflow from data preprocessing to model inference. In protein structure prediction experiments for biomedicine, the platform improved training efficiency by 40% while maintaining encrypted data storage throughout.

2. Technical Architecture and Components

2.1 Hardware Infrastructure

The computing cluster uses domestic accelerators delivering 256 TFLOPS FP16 performance per card, with high-speed interconnection achieved through NVLink alternative technology. The distributed storage system incorporates hardware-level encryption modules, maintaining 100GB/s bandwidth while implementing transparent data encryption. A typical 32-node configuration supports parallel training of models with hundreds of billions of parameters.

# Distributed training configuration example
config = {
    "cluster_spec": {
        "worker": ["node01:2222", "node02:2222"],
        "ps": ["node03:2222"]
    },
    "security_config": {
        "encryption_type": "AES-256",
        "key_management": "HSM"
    }
}

2.2 Operating System and Middleware

A security-enhanced version of domestic OS integrates mandatory access control (MAC) and audit trail modules. Kernel parameter modifications optimize IPC communication efficiency, reducing multi-process collaboration latency to under 50μs. The middleware layer provides a model serving framework supporting dynamic scaling and automatic failover.

2.3 Algorithm Framework

Deep optimization for domestic deep learning frameworks achieves operator-level improvements. For 671B parameter models, a mixed-precision training scheme reduces GPU memory usage by 40% while maintaining <0.5% accuracy loss. The dynamic graph-static graph conversion technology triples debugging efficiency.

3. Security Enhancement Strategies

3.1 Hardware-Software Co-Optimization

Atomic operation acceleration through hardware instruction set extensions improves cryptographic computing performance by 2.3x. A customized CUDA alternative library for domestic accelerators achieves 92% performance of international mainstream products. Tensor core fusion technology stabilizes end-to-end inference latency below 8ms.

3.2 Security Evaluation Framework

The comprehensive security framework covers model lifecycle:

• Training data poisoning detection: Outlier clustering algorithms
• Model backdoor detection: Neuron activation pattern analysis
• Adversarial sample defense: Input purification + robust training

In financial risk control model testing, the system blocked 99.7% of adversarial attacks while achieving industry-leading robustness metrics.

3.3 Content Security System

The multi-layer content filtering system includes:

1. Input layer: Semantic-based sensitive information detection
2. Model layer: Value alignment training
3. Output layer: Dynamic content review engine

In medical consultation scenarios, this approach achieves 98.5% violation detection rate while maintaining >95% response usability.

4. Trusted Computing Implementation

4.1 Data Lifecycle Protection

Security controls begin at data collection:

• Collection: Hardware TEE environment
• Transmission: SM4 encrypted channels
• Storage: Distributed key management system
• Usage: Dynamic data masking engine

In genomic sequencing experiments, the system enabled secure sharing of PB-scale data while meeting HIPAA compliance requirements.

4.2 Collaboration Mechanism

A blockchain-based permission management system implements fine-grained access control:

// Smart contract example: Data access control
contract DataAccess {
    mapping(address => mapping(bytes32 => bool)) public permissions;
    function grantAccess(address user, bytes32 dataHash) public {
        require(msg.sender == owner, "Only owner can grant access");
        permissions[user][dataHash] = true;
    }
}

Audit logs stored on-chain ensure complete operation traceability.

4.3 Performance Optimization

Custom optimizations for domestic hardware include:

• Memory management: Specialized allocator reducing fragmentation
• Communication: RDMA technology lowering network latency
• Computation: Redesigned convolution algorithms improving density

Benchmark tests show 3,200 images/sec throughput for ResNet-152 training.

5. Application Scenarios and Evaluation

For COVID-19 variant prediction in biomedicine:

• Training data: 1.2PB sequence data
• Model parameters: 85 billion
• Training time: Reduced from 21 to 7 days
• Security: Zero data leakage incidents

In financial technology, the intelligent advisory system achieves:

• Response latency: <200ms (99th percentile)
• Availability: 99.99%
• Compliance: Meets CBIRC AI governance guidelines

6. Future Development

Three evolution paths are planned:

1. Heterogeneous computing support for domestic CPUs
2. AI-driven automated security configuration
3. Privacy computing integration combining federated learning and MPC

The 2.0 version expected in 2026 will support trillion-parameter model training and achieve Grade 3 security certification, providing enhanced protection for critical industries. This platform establishes new benchmarks for localized AI infrastructure while its modular design enables rapid replication by other research institutions.