XCZU7CG-1FFVC1156I
| Parameters | |
|---|---|
| Factory Lead Time | 1 Week |
| Package / Case | 1156-BBGA, FCBGA |
| Operating Temperature | -40°C~100°C TJ |
| Packaging | Tray |
| Published | 2016 |
| Series | Zynq® UltraScale+™ MPSoC CG |
| 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 | 360 |
| Speed | 500MHz, 1.2GHz |
| RAM Size | 256KB |
| Core Processor | Dual ARM® Cortex®-A53 MPCore™ with CoreSight™, Dual ARM®Cortex™-R5 with CoreSight™ |
| 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, 504K+ Logic Cells |
| RoHS Status | ROHS3 Compliant |
This SoC is built on Dual ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight? core processor(s).
Dual ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight? core processor(s) are used in the construction of this SoC.There is a 1156-BBGA, FCBGA package assigned to this system on a chip by the manufacturer.A SoC chip with 256KB RAM is provided for users to enjoy reliable performance.The internal architecture of this SoC design is based on the MCU, FPGA technique.Zynq? UltraScale+? MPSoC CG is the series number of this system on chip SoC.Temperatures should be -40°C~100°C TJ on average for this SoC meaning.A key point to note is that this SoC security combines Zynq?UltraScale+? FPGA, 504K+ Logic Cells.An advanced Tray package houses this SoC system on a chip.360 I/Os are included in this SoC part.
Dual ARM? Cortex?-A53 MPCore? with CoreSight?, Dual ARM?Cortex?-R5 with CoreSight? processor.
256KB RAM.
Built on MCU, FPGA.
There are a lot of Xilinx Inc.
XCZU7CG-1FFVC1156I System On Chip (SoC) applications.
- Sensor network-on-chip (sNoC)
- Industrial AC-DC
- Fitness
- Cyberphysical system-on-chip
- Networked Media Encode/Decode
- Special Issue Editors
- Efficient hardware for training of neural networks
- Industrial transport
- Deep learning hardware
- Microcontroller based SoC ( RISC-V, ARM)
