How SD NAND is Applied in AI

With the rise of AI models like ChatGPT and DeepSeek, AI application hardware has seen rapid development. Many manufacturers have launched AI products tailored for specific markets. SD NAND primarily serves as the data and model storage layer in AI applications, playing a crucial role in edge AI and embedded AI.

The Difference between SD NAND and Traditional Storage

Why choose SD NAND?

1. Large capacity meets the needs of large models: While traditional NOR Flash has limited capacity and eMMC offers high capacity at a higher cost, SD NAND provides flexible storage options that perfectly align with AI products' requirements for storage operating systems, program code, GUI resources, and cached audio-video data.
2. Edge AI delivers high cost-effectiveness with controllable expenses: The BOM considerations of edge AI necessitate SD NAND's superior cost-performance ratio, which is ideal for large-scale deployments. With lower per-unit costs than SSDs, it better suits mass production of AI devices while supporting GB-to-TB scale models and data.
3. High reliability and stability, supporting industrial-grade and pSLC applications:Built to withstand extreme temperatures and vibrations with 24/7 operation, SD NAND employs surface-mount technology that eliminates mechanical failures common in traditional plug-and-play slots. This design makes it particularly suitable for industrial environments and automotive applications requiring shock resistance and wide-temperature performance.
4. Flexible scalability with rapid AI model and data iteration: SD supports hot-swappable models, allows offline data updates, and enables easy data recovery for retraining.

What are the key features of AI products in read/write operations for memory chips? How does SD NAND fulfill these requirements?

Core Features of AI Products in Storage Chip Read/Write Scenarios

AI products' interactive information has several key features:

1. Display content is highly unpredictable, as it cannot anticipate what will be sent from the cloud. Unlike video playback, where the data stream's location determines the displayed content at any given moment, AI products' user interactions are entirely random. 
2. The final GUI interface consists of multiple display elements. Given hardware limitations and the need for richer interactive effects in AI products, combining different display elements into a cohesive interface proves to be a cost-effective solution. 
3. The system ensures rapid response times. In real-world scenarios, user interactions involve data upload/downloads and cloud processing. Slow loading would significantly degrade user experience.

Key features of AI product writing scenarios:

Currently, AI products are predominantly used for OTA updates. Given AI's rapid development, these updates occur more frequently than in conventional electronics. This necessitates faster write speeds in storage devices to prevent disruptions during the upgrade process.

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How does SD NAND satisfy AI's read requirements?

• For "bulk read" operations: 
• Stable sequential read performance with mature technology 
•  Non-limiting speed but strong endurance. 

• For "high-frequency random read": 
• SD controller's internal cache 
• Optimized access scheduling. 

• For "read reliability": SD NAND features built-in: 
• ECC error correction 
• Bad block management 
• Data parity.

How does SD NAND satisfy AI's write requirements?

• For "small and scattered writes": 
• Controller auto-merge writes 
• Reduce write amplification. 
• For "long-term writes": 
• Wear Leveling 
• Industrial-grade SD/ pSLC NAND optional.
• For "sudden writes & OTA": 
• Supports large file writes 
• Mature file system. 
• For "power loss risk": 
• Controller protection mechanism 
• Data integrity verification.