The team at Retrocausal built a computer vision platform that allows any manufacturer to rapidly set up an activity recognition pipeline to assist with manual assemblies. Using any standard camera pointed at an assembly station, the system can learn the assembly procedure and give real-time feedback. At Deci, they are tackling the problem of taking a Machine Learning model optimized for a single hardware platform and adapting it to work on a different hardware platform. When developing an ML model running on the edge, a time-consuming development cycle is made to optimize the model to run at peak capacity. However, over time hardware platforms get updated and companies need to make the decision of whether or not to update their hardware and dedicate engineering resources to manually tweak a multitude of settings.

Today at the O'Reilly AI Conference in New York, Baidu Research announced DeepBench, the first open source benchmarking tool for evaluating deep learning performance across different hardware platforms. The announcement was made during a presentation by Greg Diamos and Sharan Narang of Baidu Research's Silicon Valley AI Lab. "Deep learning developers and researchers want to train neural networks as fast as possible. Right now we are limited by computing performance," said Dr. Diamos. He added: "The first step in improving performance is to measure it, so we created DeepBench and are opening it up to the deep learning community. We believe that tracking performance on different hardware platforms will help processor designers better optimize their hardware for deep learning applications."