XCZU4EV-2SFVC784I
| Parameters | |
|---|---|
| Factory Lead Time | 1 Week |
| Package / Case | 784-BFBGA, FCBGA |
| Operating Temperature | -40°C~100°C TJ |
| Packaging | Tray |
| Published | 2016 |
| Series | Zynq® UltraScale+™ MPSoC EV |
| Part Status | Active |
| Moisture Sensitivity Level (MSL) | 4 (72 Hours) |
| HTS Code | 8542.31.00.01 |
| Peak Reflow Temperature (Cel) | NOT SPECIFIED |
| Time@Peak Reflow Temperature-Max (s) | NOT SPECIFIED |
| Number of I/O | 252 |
| Speed | 533MHz, 600MHz, 1.3GHz |
| RAM Size | 256KB |
| Core Processor | Quad ARM® Cortex®-A53 MPCore™ with CoreSight™, Dual ARM®Cortex™-R5 with CoreSight™, ARM Mali™-400 MP2 |
| Peripherals | DMA, WDT |
| Connectivity | CANbus, EBI/EMI, Ethernet, I2C, MMC/SD/SDIO, SPI, UART/USART, USB OTG |
| Architecture | MCU, FPGA |
| Primary Attributes | Zynq®UltraScale+™ FPGA, 192K+ Logic Cells |
| RoHS Status | ROHS3 Compliant |
This SoC is built on Quad ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight?, ARM Mali?-400 MP2 core processor(s).
Based on the core processor(s) Quad ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight?, ARM Mali?-400 MP2, this SoC has been developed.Its package is 784-BFBGA, FCBGA.With 256KB RAM implemented, this SoC chip provides users with a high level of performance.As far as its internal architecture is concerned, this SoC design employs the MCU, FPGA technique.Zynq? UltraScale+? MPSoC EV is the series name of this system on chip SoC.Typical operating temperatures for this SoC meaning should be -40°C~100°C TJ.This SoC security combines Zynq?UltraScale+? FPGA, 192K+ Logic Cells and that is something to note.It is packaged in a state-of-the-art Tray package.In total, this SoC part has 252 I/Os.
Quad ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight?, ARM Mali?-400 MP2 processor.
256KB RAM.
Built on MCU, FPGA.
There are a lot of Xilinx Inc.
XCZU4EV-2SFVC784I System On Chip (SoC) applications.
- Samsung galaxy gear
- Digital Media
- ARM processors
- Efficient hardware for training of neural networks
- Functional safety for critical applications in the aerospace
- Microcontroller based SoC ( RISC-V, ARM)
- Industrial transport
- Apple smart watch
- Mobile computing
- Vending machines
