東芝情報システム Product Lineup

Are there any issues with "optimization and decision-making" in manufacturing sites and control systems? ● There are too many combinations, making it impossible to provide optimal decisions in real-time. ● Calculations take too long, resulting in control delays and performance degradation. ● We want to perform high-precision optimization, but cannot use servers or the cloud. ● In embedded environments, optimization processes are heavy, leading to abandonment of implementation. 　　　　　　　　　　　　　▼ Leave it to our combination optimization solution for embedded systems! ――Features―――――――――――――――――――――― ◆ Our quantum-inspired optimization technology rapidly calculates good solutions for combination optimization problems. ◆ Supports low-latency and real-time processing, contributing to advanced control and decision-making. ◆ Compatible with various hardware such as FPGA and CPU, making it implementable in embedded environments. ――――――――――――――――――――――――――― This is a combination optimization solution for embedded devices utilizing Toshiba's unique quantum-inspired optimization technology. It can process combination optimization problems such as allocation, route exploration, and matching quickly and securely at the edge in embedded devices like automotive equipment, industrial machinery, and robots.

Do you have any of the following concerns? ● Processing sensor data and 3D data takes time ● Improving prediction accuracy slows down processing ● AI cannot be used in devices that require real-time performance ▼ Leave it to our ultra-fast, high-precision AI prediction technology "RealtimeForesight"! ―― Features ――――――――――――――――――――――― ◆ Our unique algorithm achieves both fast processing and high-precision predictions ◆ Small-scale AI model capable of real-time processing ◆ Easy implementation on edge devices and FPGAs ――――――――――――――――――――――――――――― RealtimeForesight is a technology that predicts 3D data that changes over time using AI technology.

Do you have any concerns about the performance and cost of DNN model development? ● Increasing processing speed reduces inference accuracy ● No effect of acceleration in the target environment (edge devices) ● Repeated manual model modifications, measurements, and evaluations lead to increased labor and time ● Difficult to optimize for embedded devices with limited memory 　　　　　　　　　　　　　▼ Leave it to our "DNN Acceleration Service" to shorten processing time while maintaining accuracy with our unique neural architecture search technology! ――― Features ―――――――――――――――――――― ◆ Accelerate while maintaining accuracy ◆ Provide optimal models in a short period ◆ Optimized for embedded and edge applications ―――――――――――――――――――――――――― The DNN Acceleration Service generates numerous modified models from your DNN model and searches for a DNN model that meets accuracy and performance requirements. By utilizing our unique automation and prediction technologies, we can explore the optimal DNN model in a short time. *DNN: Deep Neural Network*

Do you have challenges with "creating training data" in the fields of embedded systems, automotive, and robotics? ● Creating training data is time-consuming and costly, hindering development ● Manual annotation leads to variability in quality ● A large amount of training data is needed, but cannot be handled manually ● You want to accelerate PoC and validation, but data preparation is a bottleneck 　　　　　　　　　　　▼ Leave it to our automatic annotation service! ―― Features ――――――――――――――――――――――― ◆ Our unique AI technology automatically generates high-precision training data from a small number of training examples ◆ Supports semantic segmentation and bounding boxes ◆ Creates a large volume of training data in a short time, significantly shortening AI development periods ――――――――――――――――――――――――――――― The automatic annotation service is an engineering service that provides a large volume of high-precision training data for image annotation in a short period of time, using tools based on Toshiba Information Systems' unique AI technology, with a small dataset of learning data and target image data provided according to your annotation specifications.