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【Singapore】Asia Pacific Atlas Bootcamp - Q&A

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1. Does Atlas provide GPIO interface?

Answer: Yes. some certain type supports it.

2. How about Atlas power consumption?

Answer: Atlas power consumption is very low, especially for edge computing scenarios. see the brochure in attachment.

3. Does Atlas support a customized layer of AI framework?

Answer: yes, Atlas TBE (Tensor Boost Engine) can support it. The senior developers can develop their own customized operators by TBE.

4. Is it easy to migrate to Atlas from NVIDIA TX2?

Answer: Generally, it's not hard for migration. here is the main difference between GPU and Huawei NPU, and we also provide tools for better migration.


5. Does Atlas support robotics-related AI applications?

Answer: Atlas has many different types for different scenario, for example, Atlas 200 is a kind of embedded module, which can be plugged in the carrier board or motherboard. Atlas 200 DK has a built-in carrier board, which can provide a 40-pin serial port for robotic device connection.

6. Does Atlas support real-time AI applications of the edge? Can it store the data locally? if can, how do you ensure the low latency of edge AI?

Answer: generally, Atlas 500 and Atlas 200 are suitable for edge AI scenario, and can support real-time AI applications of the edge. Atlas 500 can support 6TB or 12TB HDD optional for local data storage. In the real world, many factors will impact on edge AI latency, we can support ms-level latency for the AI inference process if our computing power can meet the specific requirement.

7. Does Atlas support text-related AI applications?

Answer: Atlas can support limited NLP model operators, and which will be completed before June of 2020. 

8. Does Atlas support the audio-related AI applications?

Answer: yes.

9. Does Atlas support FP64 or FP32 model conversion?

Answer: Atlas can support FP32 model conversion, but cannot support FP64 conversion.

10. Does Atlas support Keras or Keras+Tensorflow conversion?

Answer: yes.

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