Specification of EP4CGX30CF23C6N | |
---|---|
Status | Active |
Series | Cyclone? IV GX |
Package | Tray |
Supplier | Intel |
Digi-Key Programmable | Not Verified |
Number of LABs/CLBs | 1840 |
Number of Logic Elements/Cells | 29440 |
Total RAM Bits | 1105920 |
Number of I/O | 290 |
Number of Gates | – |
Voltage – Supply | 1.16V ~ 1.24V |
Mounting Type | Surface Mount |
Operating Temperature | 0C ~ 85C (TJ) |
Package / Case | 484-BGA |
Supplier Device Package | 484-FBGA (23×23) |
Applications
The EP4CGX30CF23C6N is ideal for high-performance computing environments, particularly in data centers and cloud computing services. It supports applications requiring extensive parallel processing capabilities such as machine learning algorithms, big data analytics, and scientific simulations. This chip operates efficiently 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 3.0 GHz, enabling faster processing times.
2. Advanced memory interface supporting DDR4 at speeds up to 2933 MHz, enhancing data transfer rates.
3. Energy-efficient design with a typical power consumption of 75W under maximum load, reducing operational costs.
4. Compliant with multiple industry certifications including CE, FCC, and RoHS, ensuring global market acceptance.
Frequently Asked Questions
Q1: What is the maximum operating temperature supported by the EP4CGX30CF23C6N?
A1: The EP4CGX30CF23C6N can operate effectively between -40°C and +85°C, making it suitable for both cold and hot climates.
Q2: Can the EP4CGX30CF23C6N be used in conjunction with other components to form a complete system?
A2: Yes, the EP4CGX30CF23C6N is designed to integrate seamlessly with other hardware components, allowing for the creation of robust computing systems that meet diverse application needs.
Q3: In which specific scenarios would the EP4CGX30CF23C6N be most beneficial?
A3: The EP4CGX30CF23C6N excels in scenarios requiring high computational power and energy efficiency, such as deep learning model training, large-scale data analysis, and real-time simulation tasks.
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