Mitsubishi says it has developed a high-speed training algorithm for automotive deep learning that incorporates necessary inference functions for identification, recognition and prediction of unknown facts based on known facts.

The new algorithm is expected to simplify the implementation of deep learning in vehicles, industrial robots and other machinery by drastically reducing memory usage and the computational time for training. It also will enable low-cost solutions in which artificial intelligence (AI) systems with training functions perform high-level inference directly within the embedded system according to the embedded system’s peripheral environment.

The algorithm reduces the training time, computational cost and memory requirements to approximately one-thirtieth of that of conventional AI, as it can achieve a further reduction of approximately 30 percent from Mitsubishi Electric’s existing Compact AI, which itself reduced the computational cost and memory requirements for image recognition by 90 percent compared to conventional AI, according to Mitsubishi Electric’s own research as of October 14.

The new algorithm adapts to the specific purposes of each system, because it uses learning data and high-level inferences about the operating environment. This advantage will help to support the effective structuring of networks and reduce the trial and error of design, says the company.