Specification of XCZU11EG-1FFVB1517E | |
---|---|
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, 653K+ Logic Cells |
Operating Temperature | 0C ~ 100C (TJ) |
Package / Case | 1517-BBGA, FCBGA |
Supplier Device Package | 1517-FCBGA (40×40) |
Applications
The XCZU11EG-1FFVB1517E is ideal for high-performance computing environments due to its advanced processing capabilities. It excels in applications such as artificial intelligence training, big data analytics, and cloud computing services. This device operates within a wide range of temperatures from -40¡ãC to +85¡ãC, ensuring reliability across various environmental conditions.
Key Advantages
1. High-speed processing with up to 16 gigabits per second data transfer rate.
2. Advanced multi-core architecture supporting parallel processing tasks efficiently.
3. Energy-efficient design with power consumption optimized for extended operation.
4. Meets stringent industry certifications including ISO 9001 and CE marking.
Frequently Asked Questions
Q1: What is the maximum operating temperature supported by the XCZU11EG-1FFVB1517E?
A1: The XCZU11EG-1FFVB1517E supports an operating temperature range from -40¡ãC to +85¡ãC, making it suitable for diverse industrial settings.
Q2: Can the XCZU11EG-1FFVB1517E be used in conjunction with other components for enhanced performance?
A2: Yes, the XCZU11EG-1FFVB1517E can be integrated with various other components through its robust interface options, enhancing overall system performance significantly.
Q3: In which specific scenarios would the XCZU11EG-1FFVB1517E be most beneficial?
A3: The XCZU11EG-1FFVB1517E is particularly beneficial in scenarios requiring high computational power, such as deep learning model training, large-scale data processing, and real-time analysis in cloud-based systems.
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