Sinovoip Launches Banana Pi EAI80

Sinovoip has launched its latest Banana Pi model, which is a $16 board featuring Edgeless’ EAI80 microcontroller with 300 gigaoperations per second (GOPS) neural processing unit (NPU) on-board. The core of the Banana Pi EAI80 is the Edgeless EAI80 system-on-chip (SoC), which consists of a dual-core Arm Cortex-M4F primary processor clocking at 200MHz, and a 300MHz neural processing unit (NPU) designed to accelerate convolutional neural network (CNN) workloads with up to 300 GOPS of compute performance.

The board features 8MB of SDRAM memory, a 40-pin GPIO header, an LCD connector offering up to 1024x768px resolution, CAN bus header, two on-board microphones, and a DVP camera connector. The board is powered by a USB 2.0 Type-C connector, with network connectivity enabled by an on-board Espressif ESP8266 Wi-Fi module. The board runs uGelis embedded OS, and you can also program the board using the Kelis SDK which includes AI audio and computer vision demos for word recognition, hand gesture detection and recognition, and human body detection.

Target Applications of the Banana Pi EAI80 includes:

• 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

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

Hardware specs includes:

• 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

The board is available on AliExpress for $16, and comes with an all-in-one educational kit, which contains the board, touchscreen display, camera, and USB power supply for $80. You can find more information on the Banana Pi wiki.

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