1 Introduction BPI-EAI80
- 1.1 Target Applications
2 Hardware
- 2.1 Hardware Spec
- 2.2 Hardware interface
3 Source code
4 Software
- 4.1 Linux SDK Compile and burn
- 4.2 BPI-EAI80 board , EAISeries SDK UserGride
5 About Edgeless EAI series
- 5.1 EAI series Documents
6 BPI-EAI80 documents
7 easy to buy sample

Introduction BPI-EAI80

BPI-EAI80 AI board Gree EAI80 chip design

BPI-AI Kendryte K210 RISC-V

BPI-Bit with ESP32 design

BPI-K210 RISC-V AIoT board

BPI-EAI80 AIoT board use Edgeless EAI80 chip design .it have Dual-Cortex M4F@200MHz 500DMIPS and AI-NPU：CNN-NPU @300 MHz 300GOPS. support LVDS pannel and camera interface. onboard wifi

Target Applications

Voice Control - Key words real-time control
Computer Vision - Detection & recognition of object and biology (Face, Body, Gesture), vSLAM
End-side AIoT - Edge computing, Info. Security,Off-line equipment control, System monitor
Sensor, Attendance machine, AD display, Wearable,devices, Smart unmanned retail, STEM education
Home & Building Automation – White goods,HVAC, Lighting, Security system, IoT gateways
Industrial Computing - EBS, PLCs, M2M, T&M,Auto-factory, HMI control assembly, QR/bar code
Motor Control & Power Conversion - VFC,FOC, 3D/thermal Printers, ADAS, UAV, Robots
STEAM education

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 )
2 Mic support
Size: 86x54mm

Hardware interface

Source code

github: https://github.com/BPI-SINOVOIP/BPI-EAI80-bsp

Software

Linux SDK Compile and burn

1. EAI80 Burning Steps (Elementary)

BPI-EAI80 board , EAISeries SDK UserGride

BPI-EAI80 board , EAISeries SDK UserGride:

https://drive.google.com/file/d/18kmhdspeWu05nkt3DMM2rAd1oz-30rQR/view?usp=sharing

please install KeliSDK:

https://github.com/BPI-SINOVOIP/BPI-EAI80-bsp/tree/master/KelisSDK

https://github.com/BPI-SINOVOIP/BPI-EAI80-bsp/blob/master/Keil.EAISeries_DFP.1.4.1.pack

windows version:

https://github.com/BPI-SINOVOIP/BPI-EAI80-bsp/tree/master/KelisSDK

1. Install EAI80 SDK (windows ) 2. PLease install ※Keil.EAISeries_DFP.1.4.1.pack§first; 3. Demo board should connect with PC through J-Link and SWD interface. All the operations refer to "EAISeries_SDK_UserGuide" in detail.

EAI80 Demo:

Demo path: EAI80_SDK_v1.0\ugelis\kelis_example\ai_example\ai Demo functions:

1st.Voice - Key words recognition;
2rd.Computer Vision - Hand gesture detection and recognition;
3nd.Computer Vision - Human dody detection.

About Edgeless EAI series

EAI Series adopt“Dual-CPU+NPU”structure, combine AI ability with real-time and low power embedded MCU. Rich peripherals and HW-security integrated, EAI series are designed to support next-gen. IoT applications, provide single-chip turnkey solution with AI recognition and MCU control. Edgeless Embedded AI Series crossover processor, EAI series,under the wave of AIoT, are launched for smart home, industry, stem education, energy MGT and etc., with AI ability, low power and cost-effective, provide complete HW/SW turnkey solutions. Target to establish the smallest AI+MCU module in the world, promote transboundary innovation, empower end-sides and industries.

More advanced structure and performance
Lower run and standby power consumption
Voice / Computer vision / 2D Graph accelerator
Real-time operation / Multiple HW-level security
Easy to use / Richer integration / Lower cost

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