Specification of XCZU5EV-1SFVC784E | |
---|---|
Status | Active |
Series | Zynq? UltraScale+? MPSoC EV |
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, 256K+ Logic Cells |
Operating Temperature | 0C ~ 100C (TJ) |
Package / Case | 784-BFBGA, FCBGA |
Supplier Device Package | 784-FCBGA (23×23) |
Applications
The XCZU5EV-1SFVC784E is ideal for high-performance computing environments such as cloud servers, data centers, and AI training systems. It excels in applications requiring high-speed data processing and large-scale parallel computing tasks. This device supports operating temperatures ranging from -40°C to +85°C, making it suitable for various industrial settings.
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 core, enhancing energy efficiency.
4. Compliance with multiple industry-standard certifications ensuring reliability and safety.
Frequently Asked Questions
Q1: What is the maximum operating temperature supported by the XCZU5EV-1SFVC784E?
A1: The XCZU5EV-1SFVC784E operates within a temperature range of -40°C to +85°C, ensuring robust performance across diverse environments.
Q2: Can the XCZU5EV-1SFVC784E be used in conjunction with other components for enhanced functionality?
A2: Yes, the XCZU5EV-1SFVC784E can integrate seamlessly with various peripheral devices and software platforms, expanding its capabilities significantly.
Q3: In which specific scenarios would the XCZU5EV-1SFVC784E be most beneficial?
A3: The XCZU5EV-1SFVC784E is particularly advantageous in scenarios involving real-time data analysis, machine learning model training, and high-frequency trading systems due to its high-speed processing and low-latency characteristics.
Other people’s search terms
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