- Xilinx Zynq UltraScale+ ZU3EG MPSoC based on 1.2 GHz Quad Arm Cortex-A53 and 600MHz Dual Cortex-M4 Cores
- 4GB DDR4 SDRAM (64-bit, 2400MHz)
- 8GB eMMC Flash, 32MB QSPI Flash, 32KB EEPROM
- USB2.0, USB3.0, Gigabit Ethernet, TF, DP, PCIe, MIPI-CSI, BT1120, USB-UART, JTAG…
- Computing Power up to 1.2TOPS, MobileNet up to 100FPS
- Ready-to-Run PetaLinux
- Supports Baidu's PaddlePaddle Deep Learning AI Framework
Overview
MYIR is a Xilinx Alliance Member, welcome to use MYIR's Xilinx products! http://www.xilinx.com/alliance/memberlocator/1-2wv1bc.html
The FZ3 Card is a powerful deep learning accelerator card based on Xilinx Zynq UltraScale+ ZU3EG MPSoC which features a 1.2 GHz quad-core ARM Cortex-A53 64-bit application processor, a 600MHz dual-core real-time ARM Cortex-R5 processor, a Mali400 embedded GPU and rich FPGA fabric. Besides, it integrates 4GB DDR4, 8GB eMMC, 32MB QSPI Flash and 32KB EEPROM as well as many peripherals including USB 2.0, USB 3.0, Gigabit Ethernet, TF, DisplayPort (DP), PCIe interface, MIPI-CSI, BT1120 camera, USB-UART, JTAG, IO expansion interfaces, etc. The rich resources enable users to integrate intelligent hardware easily.
FZ3 Card Top-view
FZ3 Card Bottom-view The FZ3 Card is able to run PetaLinux 2019.1 and supports PaddlePaddle deep learning AI framework which is fully compatible to use Baidu Brain’s AI development tools like EasyDL, AI Studio and EasyEdge to enable developers and engineers to quickly leverage Baidu-proven technology or deploy self-defined models, enabling faster deployment. Typical applications are AI camera, AI computing device, robotics, intelligent car, intelligent electronic scale, patrol UAV and other embedded intelligent applications. Baidu Brain’s AI development tools
Software Architecture of FZ3 Card
MYIR provides FZ3 Kit which contains the FZ3 Card with installed radiator and some necessary accessories including one power adaptor, one 16GB TF card, one mini USB cable and one mini DP to HDMI cable. It helps users start their development rapidly when getting the kit out-of-box right away.
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Features of FZ3 Card
FZ3 Card in the Video FZ3 deep learning accelerator card based on Xilinx Zynq UltraScale+ ZU3EG
Other MYIR's Xilinx Products http://www.myirtech.com/xilinxseries.asp Z-turn Board Single Board Computer (based on Zynq-7010 / 7020) Z-turn Lite Single Board Computer (based on Zynq-7007S / Zynq-7010) MYD-C7Z015 Development Board (MYC-C7Z015 CPU Module as core board) MYD-Y7Z010/20 Development Board (MYC-Y7Z010/20 CPU Module as core board) MYD-C7Z010/20 Development Board (MYC-C7Z010/20 CPU Module as core board) MYD-CZU3EG Development Board (MYC-CZU3EG CPU Module as core board) MYD-CZU4EV Development Board (MYC-CZU4EV CPU Module as core board) VECP Starter Kit - a complete Vision Edge Computing Platform (based on Xilinx Zynq UltraScale+ ZU3EG MPSoC) |
Hardware Features
Zynq® UltraScale+™ MPSoC devices provide 64-bit processor scalability while combining real-time control with soft and hard engines for graphics, video, waveform, and packet processing. Built on a common real-time processor and programmable logic equipped platform, three distinct variants include dual application processor (CG) devices, quad application processor and GPU (EG) devices, and video codec (EV) devices.
Zynq UltraScale+ MPSoCs
The Zynq UltraScale+ family provides footprint compatibility to enable users to migrate designs from one device to another. Any two packages with the same footprint identifier code (last letter and number sequence) are footprint compatible. MYIR is using the XCZU3EG-1SFVC784E MPSoC for MYD-CZU3EG Development Board by default, the C784 package covers the widest footprint compatibilities that enable users to select devices among CG, EG and EV.
Zynq UltraScale+ MPSoC Device Migration Table
MYIR may also supply the MYC-CZU3EG CPU Modules with XCZU2CG, XCZU3CG, XCZU4EV or XCZU5EV MPSoC as options. The main features for the MPSoC devices are summarized as below.
Device | XCZU2CG | XCZU3CG | XCZU3EG | XCZU4EV | XCZU5EV |
Logic cells (k) | 103 | 154 | 154 | 192 | 256 |
CLB Flip-Flops (K) | 94 | 141 | 141 | 176 | 234 |
CLB LUTs (K) | 47 | 71 | 71 | 88 | 117 |
Block RAM (Mb) | 5.3 | 7.6 | 7.6 | 4.5 | 5.1 |
UltraRAM (Mb) | - | - | - | 13.5 | 18.0 |
DSP Slices | 240 | 360 | 360 | 728 | 1,248 |
GTX transceivers | PS-GTR4x (6Gb/s) | PS-GTR4x (6Gb/s) | PS-GTR4x (6Gb/s) | PS-GTR4x (6Gb/s), GTH4x (16.3Gb/s) | PS-GTR4x (6Gb/s), GTH4x (16.3Gb/s) |
Processor Units | |||||
Application Processor Unit | Dual-core ARM® Cortex™-A53 MPCore™ up to 1.3GHz | Quad-core ARM® Cortex™-A53 MPCore™ up to 1.5GHz | |||
Memory w/ECC | L1 Cache 32KB I / D per core, L2 Cache 1MB, on-chip Memory 256KB | ||||
Real-Time Processor Unit | Dual-core ARM Cortex-R5 MPCore™ up to 600MHz | ||||
Memory w/ECC | L1 Cache 32KB I / D per core, Tightly Coupled Memory 128KB per core | ||||
Graphics Processing Unit | - | - | Mali™-400 MP2 up to 667MHz | ||
Video Codec | - | - | - | H.264 / H.265 | |
Memory L2 Cache | 64KB | ||||
External Memory, Connectivity, Integrated Block Functionality | |||||
Dynamic Memory Interface | x32/x64: DDR4, LPDDR4, DDR3, DDR3L, LPDDR3 with ECC | ||||
Static Memory Interfaces | NAND, 2x Quad-SPI | ||||
High-Speed Connectivity | PCIe® Gen2 x4, 2x USB3.0, SATA 3.1, DisplayPort, 4x Tri-mode Gigabit Ethernet | ||||
General Connectivity | 2 x USB 2.0, 2 x SD/SDIO, 2 x UART, 2 x CAN 2.0B, 2 x I2C, 2 x SPI, 4 x 32b GPIO | ||||
Power Management | Full / Low / PL / Battery Power Domains | ||||
Security | RSA, AES, and SHA | ||||
AMS - System Monitor | 10-bit, 1MSPS – Temperature and Voltage Monitor |
Zynq UltraScale+ MPSoC Device Selection Guide
Dimensions of FZ3 Card
Software Features
The FZ3 Card is able to run PetaLinux 2019.1 and supports PaddlePaddle deep learning AI framework which is fully compatible to use Baidu Brain’s AI development tools like EasyDL, AI Studio and EasyEdge to enable developers and engineers to quickly leverage Baidu-proven technology or deploy self-defined models, enabling faster deployment.
Baidu Brain’s AI development tools
Software Architecture of FZ3 Card
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