Western Digital Made RISC-V Linux & BusyBox Boot on Sipeed Maix Go Board

The other day we wrote about Getting Started with Embedded Linux on RISC-V in QEMU emulator and noted that Linux capable RISC-V hardware is currently fairly expensive. We also mentioned there was work on porting uCLinux to Kendryte K210 RISC-V processor on boards such as Sipeed Maix board. The processor only comes with 8MB RAM, and does not feature an MMU (Memory Management Unit) so what you’d be able to do on the board would be limited, and for instance, a desktop environment is clearly impossible on the platform. NOMMU support also requires some extra work, and in Linux 5.4 we saw only of the changes was “SiFive PLIC IRQ chip modifications, in preparation for M-mode Linux”. The slide above is extracted from the “RISC-V NOMMU and M-Mode Linux” presentation by Damien Le Moal, Western Digital at the Linux Plumbers Conference 2019 last September. It explains M-mode support is better suited for NOMMU mode since more direct access to the …

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Giveaway Week Winners – November 2019

giveaway week 2019

We just had another of our yearly “Giveaway week” on CNX Software with 7 prizes including Arm and RISC-V development boards, NB-IoT tracker, USB-C hub, as well as development kits based on ESP32 or ESP8266 WiSoCs. People just had to comment within a 48 hours period, and we would randomly select a winner each day. We now have all confirmed winners with a strong start from Europe, Asia catching up mid-week, before with Poland and Brazil taking the week-end prizes: Balena Fin Developer Kit – Laurent H, FRANCE WisCellular NB-IoT & eMTC GPS Tracker – Jimmy, SWEDEN MINIX NEO S1 USB-C Hub with 120GB built-in SSD – Jeroen, BELGIUM Maixduino Sipeed M1 RISC-V AI Kit – Nguyen Tung, VIETNAM ANAVI Gas Detector Starter Kit – Bumsik Kim, SOUTH KOREA Particle Mesh IoT Development Kit – Wojciech Lubicz-Lapinski, POLAND NanoPi M4V2 SBC & Metal Case Kit – Thiago Tavares, BRAZIL I checked the results for 2018 and 2017 for fun, and …

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Giveaway Week – Maixduino Sipeed M1 RISC-V AI Kit

Maixduino giveaway

For the fourth day of Giveaway Week, I’ll give out a kit comprised of Maixduino a RISC-V development board with an AI accelerator in Arduino form factor, a camera, and a 2.4″ color LCD. I tested the Maixduino kit with MicroPython, but it can also be programmed with the Arduino IDE, or Kendryte SDK. It basically allows you to run low-power AI workloads at the edge, i.e. without access to the cloud, such as face detection. To enter the draw simply leave a comment below. Other rules are as follows: Only one entry per contest. I will filter out entries with the same IP and/or email address. Contests are open for 48 hours starting at 10 am (Bangkok time) every day. Comments will be closed after 48 hours. If comments are open, the contest is still going on. Winners will be selected with random.org and announced in the comments section of each giveaway. I’ll contact the winner by email, and …

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Getting Started with Sipeed M1 based Maixduino Board & Grove AI HAT for Raspberry Pi

Grove AI HAT Face Detection

Last year we discovered Kendryte K210 processor with a RISC-V core and featuring AI accelerators for machine vision and machine hearing. Soon after,  Sipeed M1 module was launched with the processor for aroud $10. Then this year we started to get more convenient development board featuring Sipeed M1 module such as Maixduino or Grove AI Hat. Seeed Studio sent me the last two boards for review. So I’ll start by showing the items I received, before showing how to get started with MicroPython and Arduino code. Note that I’ll be using Ubuntu 18.04, but development in Windows is also possible. Unboxing I received two packages with a Maixduino kit, and the other “Grove AI HAT for Edge Computing”. Grove AI HAT for Edge Computing Let’s start with the second. The board is a Raspberry Pi HAT with Sipeed M1 module, a 40-pin Raspberry Pi header, 6 grove connectors, as well as connectors for camera and display. The USB-C port is …

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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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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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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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Maixduino SBC Combines RISC-V AI, Arduino Form Factor, and ESP32 Wireless Module

Sipeed Maixduino SBC

Last year RISC-V cores made it into low-cost hardware with neural network and audio accelerator to speed up artificial intelligence workloads at the edge such as object recognition, and speech processing. More precisely, Kendryte K210 dual-core RISC-V processor was found in Sipeed MAIX modules and boards going for $5 and up. Since then a few other variants and kits have been made available including Seeed Studio Grove AI HAT that works connected to a Raspberry Pi or in standalone mode. Seeed Studio has now released another board with Kendryte K210 RISC-V AI processor, but based on Arduino UNO form factor and equipped with an ESP32 module for WiFi and Bluetooth connectivity. Meet Sipeed Maixduino SBC. Sipeed Maixduino specifications: AI Module – Sipeed M1 with Kendryte K210 dual-core RISC-V processor @ 600 MHz, KPU Convolutional Neural Network (CNN) hardware accelerator, APU audio hardware accelerator, 8 MB general purpose SRAM including 5.9MB usable as AI SRAM memory Wireless Module – Espressif Systems …

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