Toshiba : and Riken Develop a World-class Scalable AI Technology that, After Learning, Can Adjust AI Performance and Computational Complexity According to Usage Environment

To address this problem, Toshiba and Riken have developed a scalable AI technology that can adjust computational complexity while suppressing performance degradation. With proprietary deep-learning technology, a trained AI can operate on processors with various processing capabilities while maintaining performance, and more efficient AI development can thus be expected for various systems with different uses. This technology uses a compact deep neural network (DNN)^*3 that reduces calculation amounts by decomposing a matrix representing the weight of each layer into a smaller matrix that approximates the original full-size network with as little error as possible. When creating a compact DNN, conventional technologies^*4 reduce computational complexity simply by uniformly deleting parts of matrices across all layers. By contrast, the new technology reduces approximation error by reducing computational complexity while retaining to the extent possible matrices for layers with a large amount of important information.

During training, weights for the full-size DNN are updated so as to minimize differences between correct answers and output values of the compact DNN and the full-size DNN at various size DNN. This is expected to result in well-balanced training for any size DNN. After training, the full-size DNN can be supplied by approximating the computational complexity required by individual applications. Furthermore, correspondences between computational complexity and performance can be visualized through training, and the computational performance required for target applications can be estimated, thus facilitating selection of system processors.

An internationally recognized public database^*5 of general images was used to evaluate accuracy in the task of classifying data according to the image subject. When using the proposed technology to decrease computational complexity by one-half, one-third, and one-fourth compared with the trained full-size DNN, the detection performance was decreased by 1.1%, 2.1%, and 3.3%, respectively. The corresponding decreases in performance were 2.7%, 3.9%, and 5.0% with a conventional technology. These results show that the proposed technology achieves world-class performance compared with conventional scalable AI technologies.

Toshiba and Riken will continue optimizing this technology to hardware architectures for various application in embedded and edge devices, with the aim of practical application by 2023 following verification of effectiveness in real-world tasks.