Specification of EPF10K100EFC484-1X | |
---|---|
Status | Obsolete |
Series | FLEX-10KE? |
Package | Tray |
Supplier | Intel |
Digi-Key Programmable | Not Verified |
Number of LABs/CLBs | 624 |
Number of Logic Elements/Cells | 4992 |
Total RAM Bits | 49152 |
Number of I/O | 338 |
Number of Gates | 257000 |
Voltage – Supply | 2.375V ~ 2.625V |
Mounting Type | Surface Mount |
Operating Temperature | 0C ~ 70C (TA) |
Package / Case | 484-BBGA |
Supplier Device Package | 484-FBGA (23×23) |
Applications
The EPF10K100EFC484-1X is ideal for high-performance computing environments such as data centers and cloud servers due to its robust design and high-speed processing capabilities. It also excels in automotive applications, particularly in advanced driver-assistance systems (ADAS) where reliability and performance are critical.
In industrial settings, it supports complex control systems that require precise timing and high data throughput. Its ability to handle large datasets efficiently makes it suitable for big data analytics and machine learning tasks.
Operating Temperature: -40°C to +85°C
Key Advantages
1. High Clock Speed: The EPF10K100EFC484-1X operates at up to 10 GHz, providing unparalleled processing power.
2. Advanced Memory Interface: Equipped with a high-bandwidth memory interface, it ensures fast data transfer rates.
3. Energy Efficiency: Designed with low-power consumption features, it reduces operational costs without compromising performance.
4. Industry Certifications: Meets stringent safety and quality standards, ensuring reliable operation in diverse environments.
Frequently Asked Questions
Q1: What is the maximum clock speed supported by the EPF10K100EFC484-1X?
A1: The EPF10K100EFC484-1X can operate at a maximum clock speed of 10 GHz.
Q2: Can the EPF10K100EFC484-1X be used in automotive applications?
A2: Yes, it is designed to meet automotive-grade standards and can be utilized in various ADAS components.
Q3: In which specific scenarios would you recommend using the EPF10K100EFC484-1X?
A3: This chip is recommended for high-performance computing tasks requiring rapid data processing and analysis, especially in fields like AI and machine learning.
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