Tags: bpi-eai80, aiot, board, sbc, single, board, computer, bpi-eai80, sbc, boards

BPI-EAI80 AIoT Board SBC Single Board Computer

  • ¥156
  • BPI-EAI80 AIoT board uses Edgeless EAI80 chip design .it have Dual-Cortex [email protected] 500DMIPS and AI-NPU: CNN-NPU @300 MHz 300GOPS. support LVDS panel and camera interface. onboard wifi

    Development board is equipped with CAN bus double microphone single-camera USB 2.0 Type - C, 1024 * 768 resolution touch-screen TFT - LCD interface, support arousal and battery backup domain, low power consumption biggest provide 8 MB SDRAM + 32 MB SPI Nor the combination of Flash, and to provide regular cooperate ESP8266 debug the interrupt and reset, CAN realize remote control access to network resources and OTA upgrade EAI80 built-in high-performance multimedia module, image transmission channels and dedicated including aliasing fusion scale format conversion corner detection function, such as pretreatment of audio supports up to 8 road PDM/I2S microphone, chip other functions CAN be realized through GPIO port multiplexing and simulated users and developers CAN take advantage of the MCU control the acceleration of NPU interaction, and CAN bus for industrial application before join audio and video processing and recognition ability, for the product CAN assign and speed up.

    Target Applications

    • Voice Control - Keywords 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 Spec

    • CPU Dual-Cortex [email protected] 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

    About Edgeless EAI series

    EAI Series adopts a “Dual-CPU+NPU” structure, combines 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 a 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 into EAI, which can handle image detection and recognition use deep learning methods with high performance and low energy consumption. It supports mainstream CNN models 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



    • 2 or more ¥144
    • 4 or more ¥138
    • Brand: Banana PI
    • Product Code: bpi-eai80
    • Availability: 111

    BPI-EAI80 AIoT board uses Edgeless EAI80 chip design .it have Dual-Cortex [email protected] 500DMIPS and AI-NPU: CNN-NPU @300 MHz 300GOPS. support LVDS panel and camera interface. onboard wifi

    Development board is equipped with CAN bus double microphone single-camera USB 2.0 Type - C, 1024 * 768 resolution touch-screen TFT - LCD interface, support arousal and battery backup domain, low power consumption biggest provide 8 MB SDRAM + 32 MB SPI Nor the combination of Flash, and to provide regular cooperate ESP8266 debug the interrupt and reset, CAN realize remote control access to network resources and OTA upgrade EAI80 built-in high-performance multimedia module, image transmission channels and dedicated including aliasing fusion scale format conversion corner detection function, such as pretreatment of audio supports up to 8 road PDM/I2S microphone, chip other functions CAN be realized through GPIO port multiplexing and simulated users and developers CAN take advantage of the MCU control the acceleration of NPU interaction, and CAN bus for industrial application before join audio and video processing and recognition ability, for the product CAN assign and speed up.

    Target Applications

    • Voice Control - Keywords 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 Spec

    • CPU Dual-Cortex [email protected] 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

    About Edgeless EAI series

    EAI Series adopts a “Dual-CPU+NPU” structure, combines 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 a 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 into EAI, which can handle image detection and recognition use deep learning methods with high performance and low energy consumption. It supports mainstream CNN models 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


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