Specification of XCZU9EG-2FFVC900E | |
---|---|
Status | Active |
Series | Zynq? UltraScale+? MPSoC EG |
Package | Tray |
Supplier | AMD |
Architecture | MCU, FPGA |
Core Processor | Quad ARM Cortex-A53 MPCore with CoreSight, Dual ARMCortex-R5 with CoreSight, ARM Mali-400 MP2 |
Flash Size | – |
RAM Size | 256KB |
Peripherals | DMA, WDT |
Connectivity | CANbus, EBI/EMI, Ethernet, IC, MMC/SD/SDIO, SPI, UART/USART, USB OTG |
Speed | 533MHz, 600MHz, 1.3GHz |
Primary Attributes | ZynqUltraScale+ FPGA, 599K+ Logic Cells |
Operating Temperature | 0C ~ 100C (TJ) |
Package / Case | 900-BBGA, FCBGA |
Supplier Device Package | 900-FCBGA (31×31) |
Applications
The XCZU9EG-2FFVC900E is ideal for high-performance computing environments such as cloud servers, data centers, and AI training systems. It supports applications requiring high-speed data processing and large-scale parallel computing tasks. Key technical parameters include operating temperatures ranging from -40¡ãC to +85¡ãC.
Key Advantages
1. High clock speed up to 600 MHz, providing superior computational performance.
2. Advanced multi-core architecture supporting efficient parallel processing.
3. Low power consumption per gigaflop, making it energy-efficient.
4. Compliance with industry-standard certifications like ISO 9001 and CE Marking.
Frequently Asked Questions
Q1: What is the maximum clock speed supported by the XCZU9EG-2FFVC900E?
A1: The XCZU9EG-2FFVC900E can operate at a maximum clock speed of 600 MHz.
Q2: Is the XCZU9EG-2FFVC900E compatible with existing hardware?
A2: Yes, the XCZU9EG-2FFVC900E is backward-compatible with most existing hardware interfaces and can be integrated into new designs without significant modifications.
Q3: In which specific scenarios would you recommend using the XCZU9EG-2FFVC900E?
A3: The XCZU9EG-2FFVC900E is recommended for scenarios involving real-time data analysis, machine learning model training, and high-frequency trading systems due to its high performance and low power consumption.
Other people’s search terms
– High-performance computing solutions
– AI accelerator chips
– Cloud server optimization components
– Energy-efficient computing modules
– Multi-core processor technology