Machine learning is compute-intensive and it turns out that traditional compute hardware is not well-suited for the task. In a presentation to the Hadoop Users Group UK in October, Graphcore CTO Simon ...

Edge computing is booming. The idea of taking compute out of the data center, and bringing it as close as possible to where data is generated, is seeing lots of traction. Estimates for edge ...

If you’ve looked into GPU-accelerated machine learning projects ... impressive results it can wring out of even the lowliest hardware. [Thanks to Ishan for the tip.] ...

This means being aware of the data and the machine learning tasks at hand, while also being aware of the hardware the model will be deployed on. The DeciNets announced today are geared for image ...

Hover over this diagram to see how a neural turing machine shifts its attention over its old memory values to create new values. Unfortunately, while there are a plethora of conferences and journals ...

GPUs can carry out certain highly parallel calculations much faster than Intel’s most powerful CPUs, and have been tipped as the perfect hardware companion for machine learning software. Today, most ...