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Summary of Contents for MYiR FZ3
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Page 1: Fz3 Deep Learning Computing Card User Manual
FZ3 Deep Learning Accelerator Card User Manual FZ3 Deep Learning Computing Card User Manual V1.0 MYiR Tech | www.myirtech.com... - Page 2 FZ3 Deep Learning Accelerator Card User Manual Version Change History Version Description Date V1.0 Initial Version 2020/06/17 MYiR Tech | www.myirtech.com...
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Page 3: Table Of Contents
Introduction ..................................... 4 1. Introduction to soft cores .............................. 5 Software Introduction ..........................5 Performance data on FZ3 for common models (unquantified cropping) ......5 2. Start and connect ................................6 Start-up preparation ........................... 6 Connection Method One: SSH Connection ..................6 2.2.1... -
Page 4: Introduction
FZ3 Deep Learning Accelerator Card User Manual Introduction MYIR's FZ3 is a deep learning computing card based on Xilinx Zynq Ultrascale+ CZU3EG MPSoC, which is closely cooperated with Baidu. It integrates a 4-core ARM A53 processor + GPU + FPGA architecture, with multi-core processing capabilities, FPGA Programmability and video streaming hardware decoding capabilities. -
Page 5: Introduction To Soft Cores
1. Introduction to soft cores 1.1 Software Introduction The FZ3 computing card comes with a Linux OS, users can develop applications based on the Linux OS. (Main calling process: 1. The application obtains video input -> 2. Calls the prediction library to load the model ->... -
Page 6: Start And Connect
Hardware connection method: Connect FZ3 to the host computer or router, set the computer or router IP to the same segment as FZ3, then users can login in via SSH. Detailed Steps： 2.2.1 How to connect to the internet via SSH in Windows 1. - Page 7 Default gateway=192.168.1.1 as shown in below picture: 3. Create new window in SecureCRT. Click connect->New session->Protocol(select SSH2), click next, Hostname is default IP of FZ3: 192.168.1.254, Port is 22, Username is root, password is root, then click next to login in.
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Page 8: How To Connect To The Internet Via Ssh In Mac
Google) 2. Make sure that the PC is connected to the USB UART interface of FZ3, and then click "my computer" -> Properties -> device manager to check the port number mapped in the device manager. - Page 9 FZ3 Deep Learning Computing Card User Manual 3. Open SecureCRT, create a new window Connect -> New Session -> Protocol, select serial, baud rate 115200, not selected flow control, as shown in the picture below 4. Click [finish] and [connect] button, SecureCRT will connect to the serial port on the computing card.
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Page 10: Method Of Using Minicom To Connect Serial Prot In Mac Os
Google to search it) into PC 2. Install the Minicom tool 3. Open Terminal: Launchpad->Others->Terminal, input command “minicom -s” to configure. 4. The configuration content is as below. After configuration, connect FZ3 and input minicom in terminal select Serial port setup：... -
Page 11: Copy Files
FZ3 Deep Learning Computing Card User Manual Dynamic IP configuration 3.2 Copy files FZ3 supports SSH, samba, FTP and other network protocols, can easily carry out data communication and file copy through the network. This function will be widely used in software upgrading and user customization. -
Page 12: Realize File Copy Through Samba Protocol (For Mac Os)
/home/root/workspace/sample Sample codes tools /home/root/workspace/tools Debug tools 3.4 Run Samples When user’s PC is connected to FZ3, user can run the deep learning examples which MYIR provides. It locates in /home/root/workspace/sample. Sample Remarks classification Example of classification model detection Example of target detection model 3.4.1 Example of Classification model... - Page 13 FZ3 Deep Learning Computing Card User Manual MYiR Tech | www.myirtech.com...
- Page 14 FZ3 Deep Learning Computing Card User Manual Here is an example of a configuration file. "model":"../models/resnet50", "image":"../models/resnet50/drink.jpeg", value model Directory of model combined_model Whether it is a fusion model or not, only two files are fusion models input_width Enter the image size for neural network. Following input...
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Page 15: Example Of Object Detection
1. Load driver, after the system starts, it can be loaded automatically or manually (It has been set loaded automatically by default when leaving factory) 2. Compile the example. FZ3 has the ability to compile. Go to the directory build of the sample/classification to compile After compiling, the following files will be generated in the directory build: image_classify: To read local image reasoning example。... - Page 16 FZ3 Deep Learning Computing Card User Manual Below is an example of configuration file. "model":"../models/vgg-ssd", "image":"../models/vgg-ssd/screw.jpg", value model Directory of model combined_model Whether it is a fusion model or not, only two files are fusion models input_width Enter the image size for neural network. Following input...
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Page 17: To Output Reasoning Result And Display
1. Load drive, after the system starts, it can be loaded once (the factory has automatically loaded by default) 2. Compile the example. FZ3 has the ability to compile. Go to the directory build of the sample/classification to compile After compiling, the following files will be generated in the directory build: image_classify: To read local image reasoning example。... - Page 18 FZ3 Deep Learning Computing Card User Manual 2. Display via HDMI: Connect FZ3 and HDMI monitor using active mini DP to female HDMI cable. 3. Display via VGA: connect FZ3 and VGA monitor with male mini DP to female VGA cable. MYiR Tech | www.myirtech.com...
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Page 19: Video Input Mode
3.4.1 and 3.4.2 for how to use. 3.4.4 Video input mode FZ3 supports video data input of USB, Mipi CSI, BT1120, GigE and other protocols, can be used as the processing module of video stream in various scenarios. For example, in IPC camera which supports BT1120 protocol, FZ3 can be used as a deep learning acceleration device while IPC maintains its normal functions。... - Page 20 FZ3 Deep Learning Computing Card User Manual 3.5.1.2 Create “Image classification” model Select “Image classification”->”Train now”->”Create model”, fill in the content of the model according to personal needs, and the * sign is must. Select "next" after completion MYiR Tech | www.myirtech.com...
- Page 21 FZ3 Deep Learning Computing Card User Manual 3.5.1.3 Training model Enter “Training model”, select the model category, select “Special hardware adaptation SDK”, must select “EdgeBoard (FZ)”, click “Please choose” to choose training data, after completion, check "add recognition result as other default classification", and select "start training".
- Page 22 3.5.1.5 Install the SDK in FZ3 1. The downloaded software deployment package contains SDK and demo which are easy to use. FZ3 can be quickly deployed and run after a few simple steps. The deployment package file structure is as below.
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Page 23: Run Sdk
FZ3 Deep Learning Computing Card User Manual Open the file demo.cpp 【Directory：EasyEdge-m1800- edgeboard/cpp/baidu_easyedge_linux_cpp_aarch64_PADDLEMOBILE_FPGA_v0.3.2_gcc6.2_20190518 /demo/demo.cpp】 Write license serial number 3. Put the SDK in the directory /home/root/workspace/ directory (see "file copy" above) for decompression, then start and run according to the following methods. -
Page 24: Call Http Service
2. HTTP service function is included in the deployment package, which can be run directly ./demo/easyedge_serving /home/root/workspace/EasyEdge-m1800-edgeboard/RES/ "1111-1111- The start-up is successful if the log shows as below: Now you can directly input http://{fz3 IP address}:24401 in the browser to test the model effect in H5. MYiR Tech | www.myirtech.com... -
Page 25: Http Private Service Request Description
FZ3 Deep Learning Computing Card User Manual 3.5.4 HTTP private service request description http request parameters Get parameter in URL： parameter Explanation Defaults threshold Threshold filtering， 0~1 HTTP post body is the binary content of the image (No Base64, no JSON) Request example for Python: 'http://127.0.0.1:... - Page 26 FZ3 Deep Learning Computing Card User Manual Request example for JAVA http data return Other Type The content is specific identification results Prediction time Example of return data: Errors description All active errors reported by the SDK are covered in the Edgestatus enumeration. At the same time, the SDK will show a detailed error log.
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Page 27: Advanced Guide
Video data input via GigE protocol You can choose the GIGE camera that supports the Linux system, and contact our company to adapt the official SDK of the camera. The hardware is connected to the FZ3 network port. 4.1.3 Load device driver Using FZ3's acceleration function, the prediction library will calculate the op with a large amount of calculation through the driver to call FPGA to calculate. -
Page 28: Using The Prediction Library
FZ3 supports Paddle-Moblie prediction library. The compiled prediction library is in /home/root/workspace/paddle-mobile. How to use it: Copy the header file and dynamic library of the prediction library to your own application. You can also refer to the sample MYIR provides. Paddle-Moblie source code: https://github.com/PaddlePaddle/paddle-mobile 4.1.5 Create Application 4.1.5.1 Add prediction library... - Page 29 BT1120 ipc camera After FZ3 receives the original data through BT1120 protocol and reasonig, it can transmit the result back to ipc through the serial port or spi (For interface description of BT1120, serial port, spi please refer to the hardware description). The frame number of the image can be carried in the pixel data.
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Page 30: Driver Description
2. Image preprocessing (Minus mean value, floating point conversion, normalization, etc.). 3. Output data. Because FZ3 uses NHWC format, usually the data from the video is in NHWC format, so NHWC->NCHW conversion is not required. -
Page 31: Prediction Library Description
4.3 Prediction library description 1. Paddle-Mobile Paddle-Mobile is a project under PaddlePaddle structure and is a deep learning prediction framework dedicated to embedded platforms. FZ3 uses the Paddle-Mobile prediction library implemented by FPGA under Paddle-Mobile. 2. NHWC Based on the characteristics of FPGA, Paddle-Mobile's FPGA implementation data format is NHWC. - Page 32 FZ3 Deep Learning Computing Card User Manual Based on the characteristics of FPGA, before using OP realized by FPGA, the data of Tensor needs to be aligned based on C*W by 16-bit multiple. On the contrary, the previous node is implemented by FPGA, and the next node needs to be aligned when implemented by CPU.
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Page 33: Appendix 1 Warranty & Technical Support Services
No returns or cancellations will be accepted without prior written agreement from MYIR. Delivery Time MYIR will always keep a certain stock for its regular products. If your order quantity is less than the amount of inventory, the delivery time would be within three days; if your order quantity is greater than the number of inventory, the delivery time would be always four to six weeks. - Page 34 2) Please do not use finger nails or hard sharp object to touch the surface of the LCD. 3) MYIR suggests user purchasing a piece of special wiper to wipe the LCD after long time use, please avoid clean the surface with fingers or hands to leave fingerprint.
- Page 35 During the warranty period, the shipping cost which delivered to MYIR should be responsible by user; MYIR will pay for the return shipping cost to users when the product is repaired. If the warranty period is expired, all the shipping cost will be responsible by users.
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