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A Wave of Purpose-Built AI Hardware Is Building Posted on May 16 - 2018

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Google last week unveiled the third version of its Tensor Processing Unit (TPU), which is designed to accelerate deep learning workloads developed in its TensorFlow environment. But that’s just the start of a groundswell of new processors and processing architectures, including Wave Computing, which claims its soon-to-be-launched processor will dramatically lower the barrier of entry for running artificial intelligence workloads.

Compared to traditional machine learning algorithms, deep learning models offer superior accuracy and the potential to achieve human-like precision across a range of tasks. That’s true for both major branches in the deep learning family tree, including convolutional neural networks (CNNs), which are mostly geared toward solving computer vision-type problems, and recurrent neural network (RNNs), which are geared toward language-oriented problems.

While deep learning offers better results, those results come at a cost in the form of two key ingredients that must be present to get the benefits: large amounts of data and large amounts of computing power. Without those two things, the costs likely outweigh the benefits. So it should come as no surprise that the Web giants (i.e. “the hyperscalers”) – which store most of the words and pictures we generate on the Web in their mammoth data centers — have led the way in the development and use of deep learning.

At first the hyperscalers loaded up on NVidia GPUs, which offered the processing oomph required to train big and complex deep neural networks on huge amounts of data. This stocking up on GPUs by hypescalers and high performance computing installations has been a great boon to NVidia, whose stock has risen by nearly 1,200% over the last three years.

However, not everybody is content with the performance offered by Nvidia GPUs. Intel moved strongly to bolster its deep learning chops by acquiring several firms, including spending a reported $400 million on Nervana Systems in 2016 and the $16.7-billion acquisition of FPGA maker Altera in 2015. Intel has promised a 100x improvement in deep learning training, and released the Knights Mill generation of Xeon Phi processors in December as part of that strategy. Not to be outdone, AMD is also pursuing the deep learning market with its Radeon Instinct line of GPUs. View More

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