IBM's new tool boosts deep learning speed, but only for its hardware

IBM unveiled a new technique today that's supposed to drastically reduce how much time it takes to train distributed deep learning (DDL) systems by applying a ton of powerful hardware to the task. It works by optimizing data transfer between hardware components that run a deep neural network. The key issue IBM is trying to solve is that of networking bottlenecks in distributed deep learning systems. While it's possible to spread the computational load for training a deep neural network out over many computers, that process becomes less and less efficient because of high-latency connections between the hardware doing the actual computation. PowerAI DDL, a new communication library released in conjunction with an explanatory research paper, aims to improve efficiency by making sure that the systems at play take advantage of all the high-performance connections available.