Specification of XC7Z035-2FBG676E | |
---|---|
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, 275K Logic Cells |
Operating Temperature | 0C ~ 100C (TJ) |
Package / Case | 676-BBGA, FCBGA |
Supplier Device Package | 676-FCBGA (27×27) |
Applications
The XC7Z035-2FBG676E is ideal for high-performance computing environments due to its robust processing capabilities. It excels in applications such as data centers, cloud computing services, and artificial intelligence training models. This device operates within a wide range of temperatures from -40°C to +85°C, ensuring reliability across various climates.
Key Advantages
1. High clock speed up to 200 MHz, providing superior computational performance.
2. Advanced memory interface supporting DDR4 RAM for faster data transfer rates.
3. Energy-efficient design with low power consumption per gigaflop, reducing operational costs.
4. Compliance with multiple industry certifications including CE, FCC, and RoHS.
Frequently Asked Questions
Q1: Can the XC7Z035-2FBG676E be used in extreme temperature conditions?
A1: Yes, it can operate effectively between -40°C and +85°C, making it suitable for both indoor and outdoor applications.
Q2: What kind of memory does the XC7Z035-2FBG676E support?
A2: The XC7Z035-2FBG676E supports DDR4 memory, which enhances its ability to handle large datasets efficiently.
Q3: In which specific scenarios would you recommend using the XC7Z035-2FBG676E?
A3: The XC7Z035-2FBG676E is recommended for scenarios requiring high-speed data processing, such as real-time analytics, machine learning model training, and high-frequency trading systems.
Other people’s search terms
– High-performance computing solutions
– DDR4 memory support devices
– Temperature-resistant processors
– AI training hardware
– Low-power consumption processors