Specification of EP4CE6F17A7N | |
---|---|
Status | Active |
Series | Cyclone? IV E |
Package | Tray |
Supplier | Intel |
Digi-Key Programmable | Not Verified |
Number of LABs/CLBs | 392 |
Number of Logic Elements/Cells | 6272 |
Total RAM Bits | 276480 |
Number of I/O | 179 |
Number of Gates | – |
Voltage – Supply | 1.15V ~ 1.25V |
Mounting Type | Surface Mount |
Operating Temperature | -40C ~ 125C (TJ) |
Package / Case | 256-LBGA |
Supplier Device Package | 256-FBGA (17×17) |
Applications
The EP4CE6F17A7N is ideal for high-performance computing environments due to its robust processing capabilities. It excels in applications such as data centers, cloud computing services, and AI training models. This FPGA can operate within a wide range of temperatures from -40°C to +85°C, making it suitable for various industrial settings.
Key Advantages
1. Processing Speed: The EP4CE6F17A7N offers a maximum operating frequency of up to 100 MHz, providing fast processing speeds essential for real-time applications.
2. Unique Architecture Feature: It includes advanced logic blocks that support parallel processing, enhancing overall system performance.
3. Power Efficiency: With a typical power consumption of less than 1 W at room temperature, this FPGA is energy-efficient, making it suitable for battery-powered devices.
4. Certification Standards: The EP4CE6F17A7N complies with multiple industry certifications, ensuring reliability and security in critical applications.
Frequently Asked Questions
Q1: Can the EP4CE6F17A7N be used in high-temperature environments?
A1: Yes, the EP4CE6F17A7N operates effectively within a temperature range of -40°C to +85°C, making it suitable for high-temperature applications.
Q2: What is the power consumption of the EP4CE6F17A7N?
A2: The typical power consumption of the EP4CE6F17A7N is less than 1 W at room temperature, demonstrating its energy-efficient design.
Q3: In which specific scenarios is the EP4CE6F17A7N most commonly used?
A3: The EP4CE6F17A7N is frequently used in data centers for processing large volumes of data quickly and efficiently, as well as in AI training models where high-speed computation is required.
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