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| − | = Introduction=
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| − | [[File:Banana_PI_BPI-EAI80_3.JPG|thumb|BPI-EAI80 AI board Gree EAI80 chip design]]
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| − | [[File:Banana_PI_BPI-EAI80_5.JPG|thumb|[[BPI-EAI80 AI board]] Gree EAI80 chip design]]
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| − | [[File:BPI-AI_1.JPG|thumb|[[BPI-AI]] Kendryte K210 RISC-V]]
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| − | [[File:Webduino_gif.gif|thumb|[[BPI-Bit]] with ESP32 design ]]
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| − | [[File:BPI-K210_1.JPG|thumb|[[BPI-K210 RISC-V AIoT board]]]]
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| − | =About Edgeless EAI series=
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| − | EAI series crossover AI MCU, CPU core is based on ARM Cortex-M4, ARMv7-M supports a
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| − | predefined 32-bit address space, with subdivision for code, data, and peripherals, and regions for
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| − | on-chip and off-chip resources, where on-chip refers to resources that are tightly coupled to the
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| − | processor.
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| − | EAI is a multi-core microcontroller implementing Dual-ARM Cortex-M4 cores. All cores have
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| − | access to the complete memory map. One ARM Cortex-M4 is used as the master processor. The
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| − | other ARM Cortex-M4 core can be used as a co-processor to off-load the ARM Cortex-M4 and to
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| − | perform complicated mathematical calculations.
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| − | CNN processor is integrated in EAI, which can handle image detection and recognition use deep
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| − | learning methods with high performance and low energy consumption. It supports mainstream
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| − | CNN model such as Resnet-18, Resnet-34, Vgg16, GoogleNet, Lenet etc, convolutional with kernel
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| − | size from 1 up to 7, channel/feature number up to 512, max/average pooling function with kernel
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| − | EAI chip Device Summary
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| − | [[File:Eai80_chip.png]]
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