M5Stack M5StickV is a Tiny AI Camera for Maker Projects

M5StickV Description

I’ve just started to play with Maixduino board based on ESP32 WiSoC and Sipeed M1 module that enables AI tasks such as object detection thanks to built-in AI accelerators found in Kendryte K210 RISC-V processor and noticed references to M5Stack M5StickV in firmware file names. Somehow I never wrote about M5Stack, but the company provides modular ESP32 IoT development boards that can be stacked with various modules to easily and quickly build prototypes. M5StickV is one of those modules and is similar to Maixduino kit with camera and display, minus WiFi + Bluetooth connectivity, except that everything nicely packed into a cute module. M5StickV hardware specifications: SoC – Kendryte K210 dual-core 64-bit RISC-V processor @ 400MHz with dual independent double-precision FPU, 8MB on-chip SRAM, Neural Network Processor (KPU) @ 0.8Tops, Field-Programmable IO Array (FPIOA), and more Storage – 16MB flash, microSD card slot Display -1.14″ SPI display with 240×135 resolution ( ST7789 driver) Camera – VGA (640×480) camera via OV7740 …

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Embedded Linux Conference (ELC) Europe 2019 Schedule – October 28-30

Embedded Linux Conference Europe 2019 Schedule

I may have just written about Linaro Connect San Diego 2019 schedule, but there’s another interesting event that will also take place this fall: the Embedded Linux Conference Europe on  October 28 -30, 2019 in Lyon, France. The full schedule was also published by the Linux Foundation a few days ago, so I’ll create a virtual schedule to see what interesting topics will be addressed during the 3-day event. Monday, October 28 11:30 – 12:05 – Debian and Yocto Project-Based Long-Term Maintenance Approaches for Embedded Products by Kazuhiro Hayashi, Toshiba & Jan Kiszka, Siemens AG In industrial products, 10+ years maintenance is required, including security fixes, reproducible builds, and continuous system updates. Selecting appropriate base systems and tools is necessary for efficient product development. Debian has been applied to industrial products because of its stability, long-term supports, and powerful tools for packages development. The CIP Project, which provides scalable and customizable base image and BSP layers, is now used in …

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Linaro Connect San Diego 2019 Schedule – IoT, AI, Optimizations, Compilers and More

Linaro Connect San Diego 2019

Linaro has recently released the full schedule of Linaro Connect San Diego 2019 that will take place on  September 23-27. Even if you can’t attend, it’s always interested to check out the schedule to find out what interesting work is done on Arm Linux, Zephyr OS, and so on. So I’ve created my own virtual schedule with some of the most relevant and interesting sessions of the five-day event. Monday, September 23 14:00 – 14:25 – SAN19-101 Thermal Governors: How to pick the right one by Keerthy Jagadeesh, Software Engineer, Texas Instruments With higher Gigahertz and multiple cores packed in a SoC the need for thermal management for Arm based SoCs gets more and more critical. Thermal governors that define the policy for thermal management play a pivotal role in ensuring thermal safety of the device. Choosing the right one ensures the device performs optimally with in the thermal budget. In this presentation Keerthy Jagadeesh, co-maintainer of TI BANDGAP AND …

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A Compact Machine Learning Accelerator HAT for your Raspberry Pi

Xalogic PI AI Hat

AI for the Edge has been a promising playing field where several players are pushing for. Cloud computing has made it possible to train complex machine learning models for various application, although this seems to be working fine, the performance or the possibility of deploying AI applications on the Edge is enormous. AI on the Edge is expected to help reduces the latency involved in the roundtrip to the cloud, saves the bandwidth and cloud storage costs for enterprises, deploy ML models faster, and build robust, intelligent applications. Generally, Edge devices like the Raspberry Pi, Arduinos, and other embedded boards usually can’t run powerful AI applications. They have limited resources and computing power. Fortunately, this is changing with the introduction of AI Accelerators; modern processors that help assist the edge devices by taking over the complex mathematical calculations needed for running AI models. One of such AI accelerator processor is the Kendryte K210 which has seen deployments on different development …

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TensorFlow Lite for Microcontrollers Benchmarked on Linux SBCs

TensorFlow Lite microcontrollers benchmark linux SBC

Dimitris Tassopoulos (Dimtass) decided to learn more about machine learning for embedded systems now that the technology is more mature, and wrote a series of five posts documenting his experience with low-end hardware such as STM32 Bluepill board, Arduino UNO, or ESP8266-12E module starting with simple NN examples, before moving to TensorFlow Lite for microcontrollers. Dimitris recently followed up his latest “stupid project” (that’s the name of his blog, not being demeaning here :)) by running and benchmarking TensorFlow Lite for microcontrollers on various Linux SBC. But why? you might ask. Dimitris tried to build tflite C++ API designed for Linux, but found it was hard to build, and no pre-built binary are available except for x86_64. He had no such issues with tflite-micro API, even though it’s really meant for baremetal MCU platforms. Let’s get straight to the results which also include a Ryzen platform, probably a laptop, for reference: SBC Average for 1000 runs  (ms) Ryzen 2700X (this …

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Pine64 SoEdge-RK1808 AI Module Delivers 3.0 TOPS via Rockchip RK1808 SoC

SOPINE Model A Baseboard + SoEdge-RK1808

A few weeks ago, Ameridroid reported Pine64 would soon launch SoRock and SoEdge systems-on-module, but at the time there was virtually no info except SoRock would be likely based on either RK3328 or RK3399 and work on the existing Clusterboard, while SoEdge would be an AI Neural module for Artificial Intelligence tasks, with up to 3 TeraFLOPS of performance. I did not write about it at the time, simply because there was so little information, but this morning I’ve just received some photos of SoEdge-RK1808 module fitted to a baseboard that looks to be SOPINE Model “A” carrier board. SoEdge-RK1808 SoM Let’s try to derive the specifications from the photos even though some components appear to be blurred out or just unclear: SoC – Rockchip RK1808 dual-core Cortex-A35 processor with 3.0 TOPS NPU (Neural Processing Unit) System Memory – 2GB RAM (2x 8GBit Micro DDR4-2400) but limited PC-2133 Storage – 16GB eMMC 5.1 flash (FORESEE NCEMAD9D-16G) PMIC – Rockchip RK809-2 …

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Actcast Combines IFTTT-like Service with AI and Raspberry Pi 3 / Zero

Actcast IFTTT Artificial Intelligence

In a report on April 30, 2017, Idein had developed GPGPU accelerated object recognition for the Raspberry Pi platform. That development led to the beta release of the ActCast IoT platform, which was announced on July 29, 2019, and uses deep learning algorithms for object and subject recognition. The program is for use with IoT and AI. The idea is to increase performance and link the system to the web for even more solutions. What it Does The use of physical world information in IoT projects has many applications. Such as a doorbell that sees a person, can then recognize the person. Ultimately letting the user know over the web through a smartphone, the person should be let in. And then the system unlocks the door.  So Actcast is a  bit like IFTTT with artificial intelligence / computer vision capabilities. Edge Computing Bringing the source of data closer to the point of generation, Actcast uses Edge Computing with IoT for …

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HuskyLens AI Camera & Display Board is Powered by Kendryte RISC-V Processor (Crowdfunding)

HuskyLens AI Camera

A couple of years ago, I reviewed JeVois-A33 computer vision camera  powered by Allwinner A33 quad-core Cortex-A7 processor running Linux. The tiny camera would implement easy-to-use software for machine vision with features such as object detection, eye tracking, QR code and ArUco marker detection, and so on. The camera could handle the tasks at hand, but since it relied on purely software computer vision, there were lag for some of the demo applications including 500ms for single object detection, and up to 3 seconds for YOLO test with multiple object types using deep learning algorithms. That’s a bit slow for robotics project, and software solutions usually consume more than hardware accelerated ones. Since then, we’ve started to see low-cost SoC and hardware with dedicated hardware AI accelerators, and one of those is Kendryte K210 dual-core RISC-V processor with a built-in KPU Convolutional Neural Network (CNN) hardware accelerator and APU audio hardware accelerator found in Sipeed 1 module, Maixduino SBC, and …

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