Specification of XCVU9P-L2FLGB2104E | |
---|---|
Status | Active |
Series | Virtex? UltraScale+? |
Package | Tray |
Supplier | AMD |
Digi-Key Programmable | Not Verified |
Number of LABs/CLBs | 147780 |
Number of Logic Elements/Cells | 2586150 |
Total RAM Bits | 391168000 |
Number of I/O | 702 |
Number of Gates | – |
Voltage – Supply | 0.698V ~ 0.742V |
Mounting Type | Surface Mount |
Operating Temperature | 0C ~ 110C (TJ) |
Package / Case | 2104-BBGA, FCBGA |
Supplier Device Package | 2104-FCBGA (47.5×47.5) |
Applications
The XCVU9P-L2FLGB2104E is ideal for high-performance computing environments such as data centers, cloud computing services, and artificial intelligence training. It excels in handling large-scale data processing tasks efficiently. In industrial settings, it supports real-time control systems in manufacturing plants, ensuring precise operations under varying conditions.
Operating Temperature: -40°C to +85°C
Key Advantages
1. High Performance: The XCVU9P-L2FLGB2104E offers up to 768 Gbps of memory bandwidth, enabling faster data transfer rates compared to its predecessors.
2. Scalable Architecture: Its modular design allows for easy expansion and integration into existing systems without significant modifications.
3. Energy Efficiency: With a power consumption of less than 100W at maximum load, it provides excellent energy efficiency suitable for both server rooms and mobile applications.
4. Industry Certifications: The chip has been certified to meet stringent safety and reliability standards, including ISO 9001 and CE marking.
Frequently Asked Questions
Q1: Can the XCVU9P-L2FLGB2104E be used in high-temperature environments?
A1: Yes, it operates within a wide range of temperatures from -40°C to +85°C, making it suitable for various environmental conditions.
Q2: Is there any compatibility issue with older hardware?
A2: The XCVU9P-L2FLGB2104E is backward compatible with most existing hardware interfaces, but specific drivers may need to be updated for optimal performance.
Q3: How does the XCVU9P-L2FLGB2104E perform in AI applications?
A3: The chip’s high memory bandwidth and scalable architecture make it highly effective for training complex neural networks and running deep learning algorithms efficiently.
Other people’s search terms
– High-performance computing solutions
– Scalable FPGA architectures
– Energy-efficient processors
– Industrial-grade computing chips
– AI-accelerated computing platforms