Neural Network Stick

Introduction
Product Overview
    The Bitmain Sophon Neural Network Stick (NNS) a fan less USB stick that designed for Deep Learning inference on various edge application. NNS is powered by high performance, low power Sophon BM1880 chip. BM1880 chip supports DNN/CNN/RNN/LSTM models or uniquely trained networks, and can perform facial detection, recognition, facial expression analysis, object detection, recognition, vehicle license plate recognition, voiceprint recognition, etc. The NNS enables traditional product with AI functions, and can be used in smart IPC, Robots, industrial PC, etc.
Neural Network Stick (NNS)
Product Features
Power and all data provided over a single USB type A port
Supports DNN/CNN/RNN/LSTM models profiling, compiling and tuning
Real-time inference in edge device
Quickly deploy existing DNN/CNN/RNN/LSTM models or uniquely trained networks
Features Bitmain SophonTM BM1880 with energy efficient DNN/CNN/RNN/LSTM processing
Target AI functions
Facial detection (Frame by Frame), recognition and expression analysis, such as Age, Gender, etc.
Human attributes and pose analysis
Object detection and recognition
Vehicle license plate recognition
Voice print recognition
Product Technical Specification
Processor
Sophon BM1880
Supported Framework
Caffe, ONNX, Tensorflow, Pytorch
Supported AI Models
ResNet50, Yolo V2, GoogleNet V1, MobileNet v1&v2, SSD300, AlexNet, VGG16
H.264 decoder, MJPEG encoder/decoder
1x 1080p @60fps or 2x 1080p @30fps H.264 decoder, 75fps for FHD images
Connectivity
USB3.0/USB2.0 slave mode, Type A
Dimensions
93.52715mm
Operating environmental temperature
0 – 40C (commercial level)
Hot plugin/plugoff
Yes
Certification
EMC/FCC/3C/CE
Minimum system requirement
X86_64 computer running Ubuntu 16.04 USB3.0 or USB2.0 port
Toolkit
BMNet: Bitmain Compiler which can convert supported AI models to internal format accelerated by Sophon TPU.
ONNX: Compiler which can convert ONNX format to internal format accelerated by Sophon TPU.
Quantization Tool: Convert FP32 to INT8 and support calibration function.
Get Started
Setup the  Neural Network Stick (NNS) by following the steps provided below.
Step 1: Prepare Your Equipment 
In USB mode, BMNNSDK is installed on the X86 host, and calling NNS’s computing for deep learning, the environment requirement are as follows.
Hardware environment: X86 host with usb type-A
 Bitmain Sophon Neural Network Stick (NNS)
Operating system: Ubuntu* 16.04 operating system, or Ubuntu VirtualBox instance
Package: libgoogle-glog-dev、libboost-all-dev、libprotobuf-dev, libusb-1.0-0-dev 
Bitmain Sophon Neural Network Software Development Kit (SDK), such as bmtap2-bm1880-usb-x.y.z.tar.gz  you can download the compression package from https://www.sophon.cn/drive/36.html​
Step 2:Install the BMNNSDK
You can execute below commands to install package
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$ sudo apt-get install libgoogle-glog-dev libboost-all-dev libprotobuf-dev libusb-1.0-0-dev
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There are two ways to install BMNSDK, you can have a choice
Compression package  installation
Compression package include installation script, please copy the compression package to x86, unzip and execute the script install.sh.
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$ tar zxvf bmtap2-bm1880-usb-x.y.z.tar.gz
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$ cd bmtap2-bm1880-usb-x.y.z
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$ sudo ./install.sh
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The script will install the BMNNSDK to default directory: /opt/bmtap2, and Symbolic Link will be created in the system content:
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// you will see the installation folder in the/opt/bmtap2 after successful installation
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$ ls /opt/bmtap2
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// symbolic link will be found in /usr/bin、/usr/lib、/usr/include
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$ ls -alh /usr/bin/bm*
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$ ls –alh /usr/lib/bm*  
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$ ls –alh /usr/include/bmtap2
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// if the host has BM1880 NNS，you will find device fold in the directory/dev
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$ ls /dev/ttyACM*
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/dev/ttyACM0
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To uninstall the BMNNSDK ,simply execute the uninstall script in the installation directory.
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$ sudo /opt/bmtap2/bm1880-usb-x.y.z/uninstall.sh
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 Step 3: Basic function testing
After installation the SDK, you can estimate whether the software and hardware environment is normal by running the test program in the installation directory(the file system needs to support bash for script execution correctly).
The Basic function testing use the network model and data include in the installation package for Neural Network inference, the test results will be output to the out subdirectory of the current directory, please make sure that the user has the write rights to the current directory.
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$ source /opt/bmtap2/bm1880-<Mode>-x.y.z /test/test_bmnet.sh
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$ mkdir out
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$ run_test_bmnet_bm1880
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The test results are as follow:
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# run_test_bmnet_bm1880 
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begin to test net: mtcnn det1 <1,3,12,12>
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<CMD> test_bmnet_bmodel /opt/bmtap2/bm1880-usb_1.0.1/test/../models/mtcnn/det1_input_1_3_12_12.bin /opt/bmtap2/bm1880-usb_1.0.1/test/../models/mtcnn/det1_1_3_12_12.bmodel out//det1_output.bin 1 3 12 12 
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input size: 432
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WARNING: Logging before InitGoogleLogging() is written to STDERR
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I1026 19:41:03.865362  2635 bm_firmware.cpp:76] check firmware status ...
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I1026 19:41:03.865628  2635 bm_firmware.cpp:82] firmware loading ...
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I1026 19:41:03.929674  2635 bm_firmware.cpp:47] firmware load success
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I1026 19:41:08.937716  2635 bm_firmware.cpp:98] firmware is running
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I1026 19:41:08.937757  2635 bm_device.cpp:104] device[0] opened,gmem_size : 0x40000000
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output size:6
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outputs[0]: [1,4,1,1], "conv4-2"
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outputs[1]: [1,2,1,1], "conv4-1"
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test_bmnet_bmodel: load 107 us, run 106 us, read 76 us
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('/opt/bmtap2/bm1880-usb_1.0.1/test/../models/mtcnn/det1_output_1_3_12_12_ref.bin', ' vs. ', 'out//det1_output.bin')
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Result matched @ epsilon = 0.000000
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 det1<1,3,12,12> passed
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begin to test net: mobilenet mobilenet <1,3,224,224>
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<CMD> test_bmnet_bmodel /opt/bmtap2/bm1880-usb_1.0.1/test/../models/mobilenet/mobilenet_input_1_3_224_224.bin /opt/bmtap2/bm1880-usb_1.0.1/test/../models/mobilenet/mobilenet_1_3_224_224.bmodel out//mobilenet_output.bin 1 3 224 224 
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input size: 150528
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WARNING: Logging before InitGoogleLogging() is written to STDERR
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I1026 19:41:09.092887  2662 bm_firmware.cpp:76] check firmware status ...
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I1026 19:41:09.093133  2662 bm_firmware.cpp:85] firmware is running
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I1026 19:41:09.093152  2662 bm_device.cpp:104] device[0] opened,gmem_size : 0x40000000
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output size:1000
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outputs[0]: [1,1000,1,1], "fc7"
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test_bmnet_bmodel: load 3773 us, run 2820 us, read 222 us
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('/opt/bmtap2/bm1880-usb_1.0.1/test/../models/mobilenet/mobilenet_output_1_3_224_224_ref.bin', ' vs. ', 'out//mobilenet_output.bin')
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Result matched @ epsilon = 0.000000
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 mobilenet<1,3,224,224> passed
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test_bmnet_bm1880 success
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