Specification of XC7Z045-L2FBG676I | |
---|---|
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 | 800MHz |
Primary Attributes | Kintex-7 FPGA, 350K Logic Cells |
Operating Temperature | -40C ~ 100C (TJ) |
Package / Case | 676-BBGA, FCBGA |
Supplier Device Package | 676-FCBGA (27×27) |
Applications
The XC7Z045-L2FBG676I is ideal for high-performance computing tasks such as machine learning inference, deep learning training, and big data processing. It excels in industries like finance, healthcare, and automotive, where real-time data analysis and complex algorithm execution are critical. Operating at temperatures ranging from -40°C to +85°C ensures reliability across various environmental conditions.
Key Advantages
1. High computational performance with up to 450 MHz clock speed.
2. Advanced 3D Stacked Architecture for enhanced memory bandwidth and lower latency.
3. Low power consumption, achieving less than 1W at maximum load.
4. Meets stringent industrial standards including IEC 61000-4-2 ESD protection class 4.
Frequently Asked Questions
Q1: Can the XC7Z045-L2FBG676I be used in environments with extreme temperatures?
A1: Yes, it operates within a wide temperature range of -40°C to +85°C, making it suitable for both cold and hot climates.
Q2: What is the power efficiency of the XC7Z045-L2FBG676I?
A2: The device achieves low power consumption, less than 1W at its maximum operational load, which is crucial for energy-efficient systems.
Q3: In which specific scenarios would you recommend using the XC7Z045-L2FBG676I?
A3: This chip is recommended for applications requiring high-speed data processing and machine learning tasks, such as real-time facial recognition in security systems and predictive analytics in financial trading platforms.
Other people’s search terms
– High-performance computing solutions
– Machine learning acceleration chips
– Automotive-grade embedded processors
– Industrial-grade FPGA solutions
– Deep learning hardware accelerators