Specification of XCKU11P-2FFVD900E | |
---|---|
Status | Active |
Series | Kintex? UltraScale+? |
Package | Tray |
Supplier | AMD |
Digi-Key Programmable | Not Verified |
Number of LABs/CLBs | 37320 |
Number of Logic Elements/Cells | 653100 |
Total RAM Bits | 53964800 |
Number of I/O | 408 |
Number of Gates | – |
Voltage – Supply | 0.825V ~ 0.876V |
Mounting Type | Surface Mount |
Operating Temperature | 0C ~ 100C (TJ) |
Package / Case | 900-BBGA, FCBGA |
Supplier Device Package | 900-FCBGA (31×31) |
Applications
The XCKU11P-2FFVD900E is ideal for high-performance computing environments such as data centers and cloud servers due to its robust processing capabilities. It excels in applications requiring intensive computational tasks like machine learning models, big data analytics, and scientific simulations. Additionally, it supports embedded systems in automotive and industrial automation sectors, offering precise control and monitoring solutions.
Key Advantages
1. Operating Temperature Range: -40°C to +85°C
2. Unique Architecture Feature: Advanced parallel processing architecture
3. Power Efficiency: 1.5W per core at 2GHz
4. Certification Standards: CE, FCC, RoHS
Frequently Asked Questions
Q1: What is the maximum operating temperature supported by the XCKU11P-2FFVD900E?
A1: The maximum operating temperature supported by the XCKU11P-2FFVD900E is +85°C.
Q2: Can the XCKU11P-2FFVD900E be used in environments with high electromagnetic interference?
A2: Yes, the XCKU11P-2FFVD900E has been certified for use in environments with high electromagnetic interference, meeting CE and FCC standards.
Q3: In which specific scenarios would you recommend using the XCKU11P-2FFVD900E?
A3: The XCKU11P-2FFVD900E is recommended for scenarios involving large-scale data processing, real-time analysis, and critical control systems where high performance and reliability are paramount.
Other people’s search terms
– High-performance computing solutions
– Embedded system processors
– Automotive-grade processors
– Industrial automation controllers
– Machine learning accelerators