Specification of XC7Z010-L1CLG400I | |
---|---|
Status | Active |
Series | Zynq?-7000 |
Package | Tray |
Supplier | AMD |
Architecture | MCU, FPGA |
Core Processor | Dual ARM Cortex-A9 MPCore with CoreSight |
Flash Size | – |
RAM Size | 256KB |
Peripherals | DMA |
Connectivity | CANbus, EBI/EMI, Ethernet, IC, MMC/SD/SDIO, SPI, UART/USART, USB OTG |
Speed | 667MHz |
Primary Attributes | Artix-7 FPGA, 28K Logic Cells |
Operating Temperature | -40C ~ 100C (TJ) |
Package / Case | 400-LFBGA, CSPBGA |
Supplier Device Package | 400-CSPBGA (17×17) |
Applications
The XC7Z010-L1CLG400I 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°C to +85°C, ensuring reliability across various environmental conditions.
Key Advantages
1. High clock speed up to 667 MHz, providing superior computational performance.
2. Advanced memory interface supporting DDR4 at speeds up to 2133 MT/s, enhancing data throughput.
3. Energy-efficient design with power consumption optimized for extended operation without overheating.
4. Meets stringent industrial standards including CE, FCC, and RoHS certifications.
Frequently Asked Questions
Q1: Can the XC7Z010-L1CLG400I be used in high-temperature environments?
A1: Yes, it can operate effectively between -40°C and +85°C, making it suitable for both cold and hot climates.
Q2: What are the compatibility requirements for the XC7Z010-L1CLG400I?
A2: The XC7Z010-L1CLG400I is compatible with a variety of operating systems and software platforms, including Linux, Windows, and various AI frameworks like TensorFlow and PyTorch.
Q3: In which specific scenarios would you recommend using the XC7Z010-L1CLG400I?
A3: The XC7Z010-L1CLG400I is recommended for scenarios requiring high-speed data processing, such as real-time analysis in financial markets, autonomous vehicle control systems, and large-scale machine learning model training.
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