Specification of XC5VLX50T-1FFG1136C | |
---|---|
Status | Active |
Series | Virtex?-5 LXT |
Package | Tray |
Supplier | AMD |
Digi-Key Programmable | Not Verified |
Number of LABs/CLBs | 3600 |
Number of Logic Elements/Cells | 46080 |
Total RAM Bits | 2211840 |
Number of I/O | 480 |
Number of Gates | – |
Voltage – Supply | 0.95V ~ 1.05V |
Mounting Type | Surface Mount |
Operating Temperature | 0C ~ 85C (TJ) |
Package / Case | 1136-BBGA, FCBGA |
Supplier Device Package | 1136-FCBGA (35×35) |
Applications
The XC5VLX50T-1FFG1136C is ideal for high-performance computing environments due to its advanced processing capabilities. It excels in applications such as artificial intelligence training, big data analytics, and cloud computing services. This device operates within a wide range of temperatures from -40°C to +85°C, ensuring reliability across various environmental conditions.
Key Advantages
1. High clock speed up to 750 MHz, providing superior computational performance.
2. Advanced memory interface supporting DDR4 at speeds up to 2666 MT/s, enhancing data throughput.
3. Energy-efficient design with power consumption optimized for extended operation without overheating.
4. Meets stringent industry certifications including ISO 9001 and CE marking for global compliance.
Frequently Asked Questions
Q1: Can the XC5VLX50T-1FFG1136C be used in high-temperature environments?
A1: Yes, it can operate effectively between -40°C and +85°C, making it suitable for industrial settings that require robust performance under extreme conditions.
Q2: What are the compatibility requirements for the XC5VLX50T-1FFG1136C?
A2: The XC5VLX50T-1FFG1136C is compatible with a variety of operating systems including Linux, Windows, and macOS. It also supports multiple network protocols and interfaces, ensuring seamless integration into existing IT infrastructures.
Q3: In which specific scenarios would you recommend using the XC5VLX50T-1FFG1136C?
A3: This device is particularly recommended for AI model training sessions, where its high-speed processing能力和memory bandwidth are crucial for optimizing neural network algorithms efficiently.
Other people’s search terms
– High-performance computing solutions
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– Memory-intensive processing tasks