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Sipeed MAix: AI at the edge
AI is pervasive today, from consumer to enterprise applications. With the explosive growth of connected devices, combined with a demand for privacy/confidentiality, low latency and bandwidth constraints, AI models trained in the cloud increasingly need to be run at the edge.
MAIX is Sipeed’s purpose-built module designed to run AI at the edge, we called it AIoT. It delivers high performance in a small physical and power footprint, enabling the deployment of high-accuracy AI at the edge, and the competitive price make it possible embed to any IoT devices. As you see, Sipeed MAIX is quite like Google edge TPU, but it act as master controller, not an accelerator like edge TPU, so it is more low cost and low power than AP+edge TPU solution.
MAix's Advantage and Usage Scenarios:
- MAIX is not only hardware, but also provide an end-to-end, hardware + software infrastructure for facilitating the deployment of customers' AI-based solutions.
- Thanks to its performance, small footprint, low power, and low cost, MAIX enables the broad deployment of high-quality AI at the edge.
- MAIX isn't just a hardware solution, it combines custom hardware, open software, and state-of-the-art AI algorithms to provide high-quality, easy to deploy AI solutions for the edge.
- MAIX can be used for a growing number of industrial use-cases such as predictive maintenance, anomaly detection, machine vision, robotics, voice recognition, and many more. It can be used in manufacturing, on-premise, healthcare, retail, smart spaces, transportation, etc.
- In hardware, MAIX have powerful KPU K210 inside, it offers many excited features:
- 1st competitive RISC-V chip, also 1st competitive AI chip, newly release in Sep. 2018
- 28nm process, dual-core RISC-V 64bit IMAFDC, on-chip huge 8MB high-speed SRAM (not for XMR :D), 400MHz frequency (able to 800MHz)
- KPU (Neural Network Processor) inside, 64 KPU which is 576bit width, support convolution kernels, any form of activation function. It offers 0.25TOPS@0.3W,400MHz, when overclock to 800MHz, it offers 0.5TOPS. It means you can do object recognition 60fps@VGA
- APU (Audio Processor) inside, support 8mics, up to 192KHz sample rate, hardcore FFT unit inside, easy to make a Mic Array (MAIX offer it too)
- Flexible FPIOA (Field Programmable IO Array), you can map 255 functions to all 48 GPIOs on the chip
- DVP camera and MCU LCD interface, you can connect an DVP camera, run your algorithm, and display on LCD
- Many other accelerators and peripherals: AES Accelerator, SHA256 Accelerator, FFT Accelerator (not APU's one), OTP, UART, WDT, IIC, SPI, I2S, TIMER, RTC, PWM, etc.
Inherit the advantage of K210's small footprint, Sipeed MAIX-I module, or called M1, integrate K210, 3-channel DC-DC power, 8MB/16MB/128MB Flash (M1w module add wifi chip esp8285 on it) into Square Inch Module. All usable IO breaks out as 1.27mm(50mil) pins, and pin's voltage is selectable from 3.3V and 1.8V.
Sipeed 6+1 Microphone Arra is a 6 microphone expansion board for Maix AI development boards designed for AI and voice applications.
Including 6+1 digital microphones, 12 three-color LEDs, it supports sound localization, beam forming, speech recognition etc.
MAIX support original standalone SDK, FreeRTOS SDK base on C/C++.
And we port micropython on it: http://en.maixpy.sipeed.com/. It support FPIOA, GPIO, TIMER, PWM, Flash, OV2640, LCD, etc. And it have zmodem, vi, SPIFFS on it, you can edit python directly or sz/rz file to board. We are glad to see you contribute for it:
https://github.com/sipeed/MaixPy //Maixpy project
https://github.com/sipeed/MaixPy_Doc_Us_En_Backup //Maixpy wiki project
MAix's Deep learning
MAIX support fixed-point model that the mainstream training framework trains, according to specific restriction rules, and have model compiler to compile models to its own model format.
It support tiny-yolo, mobilenet-v1, and, TensorFlow Lite! Many TensorFlow Lite model can be compiled and run on MAIX! And We will soon release model shop, you can trade your model on it.
|6 MEMS microphones : MSM261S4030H0||Sound Pressure Level : 140 dB SPL |
Sensitivity : -26(dB,dBFS @1kHz 1Pa)
Signal to noise ratio : 57 dB (20kHz bandwidth,A-weighted)
THD<1% (100dB SPL @1kHz S=Nom,Rload>2k ) Clock frequency :
150-800khz(low power mode)
|12 SK9822 LEDs||Viewing angle : 120 degree Synchronous of two-lane |
Choose postive output or negative output RGB tri-color L ED out, 8 Bit(256level) color Set, 5Bit (32 level) brightness adjustment
Current 20ma per color, 60mA total at full brigtness
|Supply voltage of external power supply||3.3V ±0.2V|
|Supply current of external power supply||>750mA(Full brightness)|
|Range of working temperature||-30℃ ~ 85℃|
|Sipeed 6+1 Microphone Array||1|
Ribbon Cable with 10pin IDC connector
- MaixPy Introduction
- Getting Started
- MaixPy Release
- MaixPy Model
- Libraries - Maix
- Libraries - Machine vision
- MicroPython Introduction
- Difference between MicroPython & CPython
- Sipeed-R6+1_MicArray_Assembly drawing 2018.11.16
- Sipeed-R6+1_MicArray_Assembly drawing 2018.11.16
- Telegram group
- FAE support email: firstname.lastname@example.org
- Kendryte K210 FreeRTOS SDK V0.5.0
- Kendryte K210 Standalone SDK V0.5.2
- Kendryte K210 datasheet English ver.V0.1.5
- Kendryte Standalone SDK Programming Guide V0.3.0
- Kendryte FreeRTOS SDK Programming Guide V0.1.0
- Kendryte OpenOCD for win32 V0.1.3
- Kendryte OpenOCD for Ubuntu x86_64 V0.1.3
- RISC-V 64bit toolchain for Kendryte K210_win32 V8.2.0
- RISC-V 64bit toolchain for Kendryte K210_ubuntu_amd64 V8.2.0
- K-Flash V0.3.0
- Kendryte K210 Model Download Guide V0.1.0
- Kendryte K210 Face Detection Demo V0.1.0
- Cmake installation
- Windows CPP Build tools