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Intel’s Latest Promise: Our First AI ASIC Chips Will Arrive in 2019

Intel announced a range of machine learning software tools and hinted at new chips on Wednesday, including its first commercial AI ASIC, the NNP-L1000, launching in 2019.

Naveen Rao, head of AI at Intel, kickstarted Chipzilla’s first AI developer conference, AIDevCon, in San Francisco. Rao was CEO and co-founder of Nervana, a deep learning startup that was acquired by Intel in 2016.

AI hype cycles have led to multiple booms and busts, Rao explained. At the moment, its popularity is rising rapidly and its greedily slurping all the data and compute available. The AI revolution is really a computing revolution. And everyone can join in on the AI fun, all you need is a bunch of CPUs, apparently.

“In fact, if you have Xeons today you don’t really need anything else to get started,” he said.

Intel’s whole spiel is that the tools needed for AI aren’t “a one size fits all problem”, instead solutions will come from a mixture of deep learning and more classical computational methods like random forest or regression analysis. And that can all be done with a smooth marriage of software and hardware.

So, here’s a quick recap of some of what was discussed today, starting with software:

  • MKL-DNNIt stands for math kernel library for deep neural networks. It’s a list of mathematic programmes for common components in neural networks, including matrix multipliers, batch norm, normalization and convolution. The library is optimised for deploying models across Intel’s CPUs.
  • nGraphDevelopers choose different AI frameworks, and they all have their own advantages and disadvantages. In order for chips to be flexible, the back-end compiler must be able to accommodate all of them effectively.

nGraph is a compiler that does this across Intel’s chips. Developers might want to train their model on Intel’s Xeons, but then use Intel’s neural network processor (NNP) for inference afterwards.

  • BigDLThis is another library for Apache Spark, aimed handling larger workloads in deep learning using distributed learning. Applications can be written in Scala or Python and executed on Spark clusters.
  • OpenVINOA software toolkit to deploy models dealing with videos on ‘the edge” aka IoT devices like cameras or mobile phones. Developers will be able to do things like image classification of facial recognition in real time. It is expected to be open sourced later this year, but is available for download now.

Now it gets hard

Now for the hardware part. Intel were more quiet on this front and didn’t divulge many details beyond the usual marketing babble.

“Xeons weren’t right for AI a couple of years ago, but that has really changed now,” Rao urged. Increased memory and compute means that there is now an increased performance of 100x since its Haswell chip and nearly a 200x rise for inference.

“You might have heard that GPUs are 100 times faster than CPUs. That is false,” he added. “Most inference is run on Xeons today.”

Without mentioning Nvidia at all, Rao explained that GPUs have had a great start in deep learning but are limited by severe memory constraints. Xeon has more memory and can scale to large batch sizes, so its better for inference, he said.

He briefly talked about FPGAs for acceleration and said Intel are working on a “discrete accelerator for inference”, but couldn’t share any details. – CT Bureau

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