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Atlas 200 Module Ascend Ai Board 8 Gb 128 Bits Lpddr4x 64mb Emmc 4.5 Ubuntu System

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Atlas 200 Module Ascend Ai Board 8 Gb 128 Bits Lpddr4x 64mb Emmc 4.5 Ubuntu System

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Brand Name : Hua Wei

Model Number : Ascend Atlas 200

Place of Origin : China

MOQ : 1set

Price : To be negotiated

Payment Terms : L/C, D/A, D/P, T/T

Supply Ability : Batch purchase price negotiation

Delivery Time : 15-30 work days

NAME : Ascend AI Board Atlas 200 Module 8 GB 128 bits LPDDR4X 64MB eMMC 4.5 Ubuntu System

Keyword : Ascend AI Board Atlas 200 Module 8 GB 128 bits LPDDR4X 64MB eMMC 4.5 Ubuntu System

Memory : 8GB 128 bits LPDDR4X

Storage : 64 MB eMMC 4.5

AI processor : Two Da Vinci AI cores Eight A55 ARM cores (maximum frequency: 1.6 GHz)

Operating temperature : -25°C to +80°C (-13°F to +176°F)

Storage temperature : -25°C to +85°C (-13°F to +185°F)

Operating humidity : 5% to 90%

Storage humidity : 5% to 95%

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Ascend AI Board Atlas 200 Module 8 GB 128 bits LPDDR4X 64MB eMMC 4.5 Ubuntu System

Hua Wei Ascend AI Board Atlas 200 Module

OS:Ubuntu 16.04

The Hua Wei Ascend Atlas 200 AI accelerator module has eight Cortex-A55 cores and provides common peripheral ports such as I2C, USB, SPI, and RGMII. It can be used as an embedded system CPU.You can burn the OS to the embedded multimedia controller (eMMC) flash or an SD card. After simple configuration, the ARM CPU in the Atlas 200 AI accelerator module can run users' AI service software.Generally, in this application mode, the Atlas 200 AI accelerator module is connected to simple external devices such as IP cameras (IPCs), I2C sensors, and Serial Peripheral Interface (SPI) displays.Powered by high-performance Huawei Ascend 310 AI Processor, the Atlas 200 AI accelerator module provides the 4 TFLOPS of FP16 and 8 TOPS for INT8, 8 TFLOPS of FP16 and 16 TOPS for INT8, as well as 11 TFLOPS of FP16 and 22 TOPS for INT8 multiply-add computing capabilities.

Provides various interfaces and supports PCIe 3.0 x4, RGMII, USB 2.0/USB 3.0, I2C, SPI, and UART interfaces.

Supports up to 16-channel 1080p@30 fps video access.

Supports H.264 and H.265 video encoding and decoding in various specifications, which can be applicable to different video processing requirements.

Hua Wei Ascend AI Board Atlas 200 Module Specification

Feature

Specification

AI processor

Ascend 310 AI Processor

Two Da Vinci AI cores

Eight A55 ARM cores (maximum frequency: 1.6 GHz)

Memory

8G 128 bits LPDDR4X

Rate: 3200 Mbit/s

Error checking and correcting (ECC)

Storage

Built-in SPI flash. Capacity: 64 MB

External MMC interfaces and supports:

– eMMC 4.5, supporting the highest-speed mode SDR50 and up to 64 GB capacity

– SD3.0 card, supporting the highest-speed mode SDR50 and up to 2 TB capacity

High-speed port

One PCIe 3.0 x4, supporting the RC or EP mode

One RGMII port

One USB 3.0 port, compatible with USB 2.0

Encoding/Decoding capability

H.264/H.265 decoder, 20-channel 1080p (1920 x 1080) 25 FPS, YUV420

H.264/H.265 decoder, 16-channel 1080p (1920 x 1080) 30 FPS, YUV420

H.264/H.265 decoder, 2-channel 4K (3840 x 2160) 60 FPS, YUV420

H.264/H.265 encoder, 1-channel 1080p (1920 x 1080) 30 FPS, YUV420

JPEG decoding at 1080p (1920 x 1080) 256 FPS and encoding at 1080p (1920 x 1080) 64 FPS, up to 8192 x 4320 resolution

PNG decoding at 1080p (1920 x 1080) 24 FPS, up to 4096 x 2160 resolution

Temperature

Operating temperature: -25°C to +80°C (-13°F to +176°F)

Storage temperature: -25°C to +85°C (-13°F to +185°F)

Other ports

  • One eMMC&SD port

  • ● Two PWM ports

  • ● Four GPIO ports

Power consumption

Operating voltage: 3.5 V to 4.5 V; recommended typical value: 3.8 V

Typical power consumption

– 4 GB: 6.5 W

– 8 GB: 9.5 W

Feature Specification

Dimensions

8.5 mm x 52.6 mm x 38.5 mm

NOTE

The connector model of the Atlas 200 AI accelerator module is fixed. You can select male connectors with different heights to determine the height of the Atlas 200 AI accelerator module.

Net weight

30g

a: stable, maximum computing power.

Basic software specifications

Feature

Specification

Operating system (OS)

Ubuntu 16.04

Deep learning framework

TensorFlow, Caffe

Compiler

CCE/CCE compiler Tool

Atlas 200 Module Ascend Ai Board 8 Gb 128 Bits Lpddr4x 64mb Emmc 4.5 Ubuntu System


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