Embedded AI & MACHINE LEARNING
 

Wednesday 7/1

 
It has been said that devices cannot do everything that humans can do. However, the devices that we use in our daily lives have been evolving over the last couple of decades. We have seen significant changes in them in terms on functionality, connectivity, and size. One of the biggest challenges, however, has been the size of the device as more efforts are put in to achieving a smaller form factor.
 
Voice enabled device testing
 
You?ve heard it many times before: AI and machine learning are moving to the Edge of the IoT. The reasons for such have been discussed numerous times, so I won?t go through it again. Suffice to say, it?s faster and cheaper and provides better performance.
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Eta Compute announced the debut of the first integrated, ultra-low power AI Sensor Board, the ECM3532 AI Sensor Board.
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Advantech announced its compact, energy-efficient MIC-710AIX and MIC-710IVX industrial computers that leverage the new NVIDIA Jetson Xavier NX.
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Aetina Corporation announced SparkBot, experimental robots developed for the medical environment. The AN110-NAO, AN310-TX2, and the AX710-XAV robots.
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Tessent software Safety ecosystem helped artificial intelligence (AI) vision silicon company Ambarella Inc. meet in-system test requirements and achieve ISO26262 automotive safety integrity level (ASIL) goals.
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IBASE Technology releases its newest EC-3200 AI computing platform, based on the NVIDIA Jetson Tegra X2 (TX2) processor. The processor couples a dual-core Denver 2 with a quad-core ARM Cortex-A57 processor.
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Through artificial intelligence, Google?s energy profiles became algorithms that allowed data center facility managers to apply well-timed instructions to the building?s mechanical and electrical plants.

While this is all pretty easy to understand, it glosses over the fact that this hyperscale provider was already hyper-efficient.
 
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