SAN JOSE, Calif. – Calling for 100x faster processors, China Web giant Baidu released DeepBench, an open source benchmark for how fast processors train neural networks for machine learning.
DeepBench is available online along with first results from Intel and Nvidia processors running it. The benchmark tests low-level operations such as matrix multiplication, convolutions, handing recurrent layers and the time it takes for data to be shared with all processors in a cluster.
Machine learning has emerged as a critical workload for Web giants such as Baidu, Google, Facebook and others. The workloads come in many flavors serving applications such as speech, object and video recognition and automatic language translation.
Today the job of training machine learning models “is limited by compute, if we had faster processors we’d run bigger models…in practice we train on a reasonable subset of data that can finish in a matter of months,” said Greg Diamos, a senior researcher at Baidu’s Silicon Valley AI Lab.
The lab has found, for example, it can reduce by 40% errors in automatic language translation for every order-of-magnitude performance improvement in computing. “We could use improvements of several orders of magnitude–100x or greater,” said Diamos. Read more of this post