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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Grove AI HAT Helps Raspberry Pi Run Edge Computing Workloads

Grove AI HAT

Last year we wrote about Kendryte K210 dual core RISC-V processor  specifically designed for for machine vision and machine hearing as well as the corresponding Kendryte KD233 which enables inference at the edge, e.g. tasks such as face recognition or object detection. Latter on we found the processor in Sipeed M1 module which went for as low as $5 in a crowdfunding campaign, and was fitted to some low cost boards now selling for $12.90 on Seeed Studio. The latter company has now designed Grove AI HAT that aims to assist Raspberry Pi in running the edge computing workloads previously described, as exposes 6 Grove interfaces to extend functionality with some of the Grove add-on modules. Grove AI HAT specifications: AI Module – Sipeed “MAIX” 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 Camera …

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MAIX Development Boards with Sipeed M1 RISC-V AI Module Launched for $5 and Up (Crowdfunding)

MAIX GO Board

Sipeed M1 (aka MAIX-I)is a compact module based on Kendryte K210 dual core RISC-V processor designed for low power artificial intelligence workloads at the edge, such as face detection, object recognition, or audio processing. The module and some development boards are available on Taobao for the Chinese market, as well as YOYCart for the rest of the world, but the company has now launched several MAIX boards based on M1 module through an Indiegogo campaign with price starting at just $5 a piece. Sipeed MAIX Bit (aka MAIX Micro) is the cheapest one at $5 (early bird) / $6 with the following specifications: SoC – Kendryte K210 dual core 64-bit RISC-V processor @ 400 MHz (overclockable up to 800 MHz) with KPU CNN hardware accelerator APU audio hardware accelerator with support for up to 8 mics, up to 192 KHz sample rate FPIOA (Field Programmable IO Array) mapping 255 functions to all 48 GPIOs on the chip. 8 MB general purpose …

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