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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.
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.
|CPU : RISC-V Dual Core 64bit, 400Mh adjustable
||Powerful dual-core 64-bit open architecture-based
processor with rich community resources
||IEEE754-2008 compliant high-performance pipelined FPU
||High-speed UART and JTAG interface for debugging
|Neural Network Processor (KPU)
||• Supports the fixed-point model that the mainstream training framework trains according to specific restriction rules
• There is no direct limit on the number of network layers, and each layer of convolutional neural network parameters can be configured separately, includ- ing the number of input and output channels, and the input and output line width and column height
• Support for 1x1 and 3x3 convolution kernels
• Support for any form of activation function
• The maximum supported neural network parameter size for real-time work is 5MiB to 5.9MiB
• The maximum supported network parameter size when
working in non-real time is (flash size - software size)
|Audio Processor (APU)
||• Up to 8 channels of audio input data, ie 4 stereo channels
• Simultaneous scanning pre-processing and beamforming for sound sources in up to 16 directions
• Supports one active voice stream output
• 16-bit wide internal audio signal processing
• Support for 12-bit, 16-bit, 24-bit, and 32-bit input data widths • Multi-channel direct raw signal output
• Up to 192kHz sample rate
• Built-in FFT unit supports 512-point FFT of audio data
•Uses system DMAC to store output data in system memory
|Static Random-Access Memory (SRAM)
||The SRAM is split into two parts, 6MiB of on-chip
general-purpose SRAM memory and 2MiB of on-chip AI SRAM memory, for a total of 8MiB
|Field Programmable IO Array (FPIOA/IOMUX)
||FPIOA allows users to map 255 internal functions to 48
free IOs on the chip
|Digital Video Port (DVP)
||Maximum frame size 640x480
||The FFT accelerator is a hardware implementation of the
Fast Fourier Transform (FFT)
|FreeRtos & Standard SDK
||Support FreeRtos and Standrad development kit.
||Support MicroPython on M1
||Machine vision based on convolutional neural network
||High performance microphone array processor
|Supply voltage of external power supply
|Supply current of external power supply
|Range of working temperature
||-30℃ ~ 85℃
||25mm x25mm x1mm
|Sipeed MAIX-I module w/o WiFi