Difference between revisions of "BPI-EAI80 AI board"
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[[File:Webduino_gif.gif|thumb|[[BPI-Bit]] with ESP32 design ]] | [[File:Webduino_gif.gif|thumb|[[BPI-Bit]] with ESP32 design ]] | ||
[[File:BPI-K210_1.JPG|thumb|[[BPI-K210 RISC-V AIoT board]]]] | [[File:BPI-K210_1.JPG|thumb|[[BPI-K210 RISC-V AIoT board]]]] | ||
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| + | =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 | ||
Revision as of 19:30, 17 April 2020
Introduction
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
