Difference between revisions of "BPI-EAI80 AIoT board"

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(Hardware)
(Hardware)
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=Hardware=
 
=Hardware=
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==Hardware Spec==
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*CPU Dual-Cortex M4F@200MHz 500DMIPS
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*AI-NPU:CNN-NPU @300 MHz 300GOPS
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*2D Graph :Dual-Camera Max
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*SDRAM 8M
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*LCD 1024*768 TFT-LCD
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*CANBUS 2.0 A/B
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*ESP8266 Wifi onboard
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*40PIN GPIO (share with LCD )
  
 
==Hardware interface==
 
==Hardware interface==

Revision as of 20:37, 20 April 2020

Introduction BPI-EAI80

BPI-EAI80 AI board Gree EAI80 chip design
BPI-EAI80 AI board Gree EAI80 chip design
BPI-AI Kendryte K210 RISC-V
BPI-Bit with ESP32 design

BPI-EAI80 board use Edgeless EAI80 chip design .

Hardware

Hardware Spec

  • CPU Dual-Cortex M4F@200MHz 500DMIPS
  • AI-NPU:CNN-NPU @300 MHz 300GOPS
  • 2D Graph :Dual-Camera Max
  • SDRAM 8M
  • LCD 1024*768 TFT-LCD
  • CANBUS 2.0 A/B
  • ESP8266 Wifi onboard
  • 40PIN GPIO (share with LCD )

Hardware interface

Banana PI BPI-EAI80 interface 750.JPG

Software

About Edgeless EAI series

EAI series crossover AI MCU, CPU core is based on ARM Cortex-M4, ARMv7-M supports a predefined 32-bit address space, with subdivision for code, data, and peripherals, and regions for on-chip and off-chip resources, where on-chip refers to resources that are tightly coupled to the processor. EAI is a multi-core microcontroller implementing Dual-ARM Cortex-M4 cores. All cores have access to the complete memory map. One ARM Cortex-M4 is used as the master processor. The other ARM Cortex-M4 core can be used as a co-processor to off-load the ARM Cortex-M4 and to perform complicated mathematical calculations. CNN processor is integrated in EAI, which can handle image detection and recognition use deep learning methods with high performance and low energy consumption. It supports mainstream CNN model such as Resnet-18, Resnet-34, Vgg16, GoogleNet, Lenet etc, convolutional with kernel size from 1 up to 7, channel/feature number up to 512, max/average pooling function with kernel

EAI chip Device Summary

Eai80 chip.png

EAI series Documents