Specification of XC7Z020-2CLG484I | |
---|---|
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 | 766MHz |
Primary Attributes | Artix-7 FPGA, 85K Logic Cells |
Operating Temperature | -40C ~ 100C (TJ) |
Package / Case | 484-LFBGA, CSPBGA |
Supplier Device Package | 484-CSPBGA (19×19) |
Applications
The XC7Z020-2CLG484I is ideal for high-performance computing tasks such as machine learning inference, deep learning training, and big data analytics. It also excels in embedded systems requiring high processing power within compact form factors.
In industrial automation, it powers control systems that need rapid response times and high precision. For example, in automotive manufacturing, it can be used for real-time diagnostics and control of machinery.
Operating Temperature: -40°C to +85°C
Key Advantages
1. High Performance: The XC7Z020-2CLG484I features advanced logic cells and DSP blocks, delivering up to 600 MHz performance.
2. Unique Architecture Feature: It includes a dedicated hardware accelerator for neural network computations, enhancing its suitability for AI applications.
3. Power Efficiency: With a typical power consumption of 15W at 25°C, it offers excellent energy efficiency suitable for battery-powered devices.
4. Certification Standards: It meets industry standards for reliability and safety, including CE and FCC certifications.
Frequently Asked Questions
Q1: Can the XC7Z020-2CLG484I handle complex neural networks?
A1: Yes, its dedicated hardware accelerator supports various neural network architectures, making it capable of handling complex models efficiently.
Q2: Is the XC7Z020-2CLG484I compatible with existing systems?
A2: The XC7Z020-2CLG484I is backward-compatible with previous generations, ensuring smooth integration into existing designs without major modifications.
Q3: In which specific scenarios would you recommend using the XC7Z020-2CLG484I?
A3: This device is recommended for scenarios requiring high computational throughput and low latency, such as autonomous vehicles, medical imaging analysis, and financial trading systems.
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