Specification of XCZU2EG-1SFVC784E | |
---|---|
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, 103K+ Logic Cells |
Operating Temperature | 0C ~ 100C (TJ) |
Package / Case | 784-BFBGA, FCBGA |
Supplier Device Package | 784-FCBGA (23×23) |
Applications
The XCZU2EG-1SFVC784E 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 enhancing parallel processing capabilities.
3. Energy-efficient design with power consumption optimized at 1.2 W per core under typical load conditions.
4. Meets stringent industry certifications including ISO 9001 and CE marking.
Frequently Asked Questions
Q1: What is the maximum clock speed supported by the XCZU2EG-1SFVC784E?
A1: The XCZU2EG-1SFVC784E supports a maximum clock speed of 600 MHz.
Q2: Can the XCZU2EG-1SFVC784E be used in environments with extreme temperatures?
A2: Yes, it operates within a wide range of temperatures from -40°C to +85°C, making it suitable for various industrial settings.
Q3: In which specific scenarios would the XCZU2EG-1SFVC784E be most beneficial?
A3: This chip excels in scenarios involving big data analytics, machine learning models, and high-frequency trading systems due to its robust performance and energy efficiency.
Other people’s search terms
– High-performance computing solutions
– AI accelerator chips
– Data center optimization technology
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– Energy-efficient computing components