Specification of EP4CE10E22A7N | |
---|---|
Status | Active |
Series | Cyclone? IV E |
Package | Tray |
Supplier | Intel |
Digi-Key Programmable | Not Verified |
Number of LABs/CLBs | 645 |
Number of Logic Elements/Cells | 10320 |
Total RAM Bits | 423936 |
Number of I/O | 91 |
Number of Gates | – |
Voltage – Supply | 1.15V ~ 1.25V |
Mounting Type | Surface Mount |
Operating Temperature | -40C ~ 125C (TJ) |
Package / Case | 144-LQFP Exposed Pad |
Supplier Device Package | 144-EQFP (20×20) |
Applications
The EP4CE10E22A7N 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 supports operations at temperatures ranging from -40°C to +85°C, ensuring reliability across various climates.
Key Advantages
1. Processing Speed: The EP4CE10E22A7N offers a maximum operating frequency of up to 667 MHz, making it suitable for high-speed data processing tasks.
2. Unique Architecture Feature: It includes advanced logic blocks that support parallel processing, enhancing computational throughput.
3. Power Efficiency: With a typical power consumption of less than 10W under standard conditions, it provides excellent energy efficiency.
4. Certification Standards: The device complies with industry-standard certifications, including CE and FCC, ensuring global compatibility.
Frequently Asked Questions
Q1: Can the EP4CE10E22A7N be used in extreme temperature environments?
A1: Yes, it operates within a wide temperature range of -40°C to +85°C, making it suitable for both cold and hot climates.
Q2: What is the maximum power consumption of the EP4CE10E22A7N?
A2: The maximum power consumption is less than 10W under standard conditions, which is very efficient for its performance level.
Q3: In which specific scenarios can I utilize the EP4CE10E22A7N effectively?
A3: The EP4CE10E22A7N is particularly effective in scenarios requiring high-speed data processing and parallel computing tasks, such as real-time analytics and machine learning model training.
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