| Parameters |
| Factory Lead Time |
11 Weeks |
| Package / Case |
784-BFBGA, FCBGA |
| Operating Temperature |
0°C~100°C TJ |
| Packaging |
Tray |
| Series |
Zynq® UltraScale+™ MPSoC EG |
| 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 |
500MHz, 600MHz, 1.2GHz |
| 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).
A Quad ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight?, ARM Mali?-400 MP2 core processor(s) is built into this SoC.Package 784-BFBGA, FCBGA is assigned to this system on a chip by the manufacturer.The 256KB RAM implementation of this SoC chip ensures efficient performance for users.The SoC design uses MCU, FPGA architecture for its internal architecture.Zynq? UltraScale+? MPSoC EG is the series number of this system on chip SoC.The average operating temps for this SoC meaning should be 0°C~100°C TJ.This SoC security combines Zynq?UltraScale+? FPGA, 192K+ Logic Cells, an important feature to keep in mind.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.
XCZU4EG-1SFVC784E System On Chip (SoC) applications.
- Keywords
- Industrial Pressure
- Smartphone accessories
- CNC control
- Automotive gateway
- Fitness
- Sensor network-on-chip (sNoC)
- Efficient hardware for inference of neural networks
- Mobile computing
- Automated sorting equipment