A Wave of Purpose-Built AI Hardware Is Building

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.