Specification of XCZU7EG-1FBVB900I | |
---|---|
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 | 500MHz, 600MHz, 1.2GHz |
Primary Attributes | ZynqUltraScale+ FPGA, 504K+ Logic Cells |
Operating Temperature | -40C ~ 100C (TJ) |
Package / Case | 900-BBGA, FCBGA |
Supplier Device Package | 900-FCBGA (31×31) |
Applications
The XCZU7EG-1FBVB900I 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°C to +85°C.
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, enhancing energy efficiency.
4. Compliance with industry-standard certifications ensuring reliability and safety.
Frequently Asked Questions
Q1: What is the maximum operating temperature supported by the XCZU7EG-1FBVB900I?
A1: The XCZU7EG-1FBVB900I operates within a temperature range of -40°C to +85°C, making it suitable for various environmental conditions.
Q2: Can the XCZU7EG-1FBVB900I be used in conjunction with other components for enhanced functionality?
A2: Yes, the XCZU7EG-1FBVB900I can be integrated with various other hardware components to enhance its capabilities, such as memory modules and additional processors, to create more powerful computing solutions.
Q3: In which specific scenarios would the XCZU7EG-1FBVB900I be most beneficial?
A3: The XCZU7EG-1FBVB900I excels in scenarios requiring intensive data processing, such as machine learning algorithms, big data analytics, and scientific simulations, due to its high performance and low power consumption.
Other people’s search terms
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