ESP-EYE Espressif AI face detection camera supporting their AI framework is in stock

There is lot of noise around Espressif new development board with 2Mpix camera which allow to develop with their ESP-WHO AI framework. ESP-EYE was announced in january and we were waiting to be released.

Now the waiting is over! We got limited quantity from their first production run and ESP-EYE is in stock.

If you want to be among the first to play with Espressif framework for Machine Learning and AI hurry up as the quantity is limited and will not last long.

Rockchip is releasing low power SOC with NPU targeting deep learning.

We are hacking cheap Chinese soldering robot aiming to make it usable with camera fiducials and solder joint inspection. I shared some info on Hackaday 2018 Belgrade conference.

As we want to make the robot easy to use we are looking around for capable SOC with Deep Learning capability. It seems the only embedded available solution now is nVidia.

Allwinner has put in their V5 SOC info about AI and Trensorflow support, but looking at info for the only available board on the market it looks just statement and no actual implementation.

The AI they advertise looks more like OpenCV / Tensorflow lite libraries using the V5 GPUs, but not real NPU.

Rockchip seems to be this time a little bit ahead of Allwinner and has released RK1808 and RK3399pro SOCs.

Some info also start to appear in their rockchip-linux repositories.

We got RK1808 brief datasheet and here are the SOC internals:

• Dual core Cortex-A35
• Internal 2MB SRAM
• DDR 32-bit data width, 2 ranks max 2GB of DDR3/DDR3L/LPDDR3/LPDDR3L -1600
• Neural Process Unit with 512KB internal buffer and Support for: max 1920 Int8, max 192 Int16 and max 64 FP16 MAC operations per cycle
• eMMC 4.5 1-4-8 bit max 150MB/s
• SD/MMC support
• SPI Flash x1-4-8 data
• video encoder/decoder up to 1080p
• video input DPI 8-10-12-16 bit up to 150MB/s
• camera input MIPI CSI up to 4 data lane, 2.0Gbps, MIPI-HS, MIPI-LP
• LCD RGB 8/8/8 up to 1280×800@60fps
• MIPI DSI 1920×1080 up to 4 data lane, 2.0GbpsA
• Audio I2S
• Gigabit Ethernet
• USB2.0 HOST/OTG
• USB3.0 5Gbps
• PCIe 1/2 links with 2.5Gbps per link
• SPI, I2C, UART
• x4 10bit SAR ADC 1Msps
• -40+125C operating temperature, targeting automotive and industrial vision apps

This chip is definitely not hobby friendly with FCCSP 420 0.3mm balls spaced at 0.5/0.35mm!

Price info is not available yet. First evaluation boards will be ready end of March 2019. Rockchip will sell SDK with the NPU API also at unknown yet price.

Rockchip also upgraded their RK3399 including inside RK1808 and naming it RK3399Pro.
They keep the same RK3399 ball layout, so people who already made RK3399 boards can upgrade with RK3399Pro without changing lot on their PCB layout.

How they do it? They bond RK1808 in the same package and connect RK3399 with RK1808 via USB3.0 this is why RK3399Pro has NO externally available USB3.0:

How they will manage power dissipation when they put together two quite power hungry chips is yet to be seen. RK3399 alone requires quite big heatsink as it dissipates up to 20W when the Cortex-A72 cores are running.

First Steps with Snap4Arduino and eduArdu tutorial in Bulgarian and video is GitHub

Our Bulgarian customers often blame us that we post and make documents in English, but we really think that if one decided to work in IT industry and deal with programming and electronics should learn English and supporting documentation in two languages just double the burden for us.

eduArdu is educational board targeting kids so it’s a different story. This is why we made small tutorial how to use eduArdu with Snap4Arduino in Bulgarian language and uploaded it to GutHub in PDF and ODT format.

On Youtube we uploaded video for the same with English text.

So we hope now both English and Bulgarian customers will be pleased 🙂

We just uploaded on Youtube video for eduArdu features and how to install and use the Arduino examples from GitHub

Free online Machine Learning course CS 229 from Stanford University

This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include:

• supervised learning (generative/discriminative learning,
• parametric/non-parametric learning, neural networks,
• support vector machines);
• unsupervised learning (clustering, dimensionality reduction, kernel methods);
• learning theory (bias/variance tradeoffs; VC theory; large margins);
• reinforcement learning and adaptive control.

The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

You can access course materials at http://cs229.stanford.edu/materials.html