Intelligent real-time decision-making with minimal resources.
Are there any challenges related to "optimization and decision-making" in systems within the embedded, automotive, and robotics fields? ● The combinations are complex, making it impossible to provide optimal decisions in real-time. ● Calculations take time, leading to control delays and performance degradation. ● You 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――――――――――――――――――― ◆ Rapid calculation of good solutions for combinatorial optimization problems using quantum-inspired optimization technology. ◆ Contributes to speeding up control and decision-making through low-latency, real-time processing. ◆ Compatible with various hardware such as FPGA and GPU, 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 combinatorial optimization problems such as allocation, route exploration, and matching at the edge, quickly and securely, in embedded devices like automotive equipment, industrial machinery, and robots.
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basic information
The combinatorial optimization problem in embedded systems is an important issue that affects the accuracy of control. Our company provides a high-speed and secure combinatorial optimization solution using Toshiba's unique quantum-inspired optimization technology. ＝＝＝＝＝＝＝＝＝Points＝＝＝＝＝＝＝＝＝＝＝＝＝ Point 1: Rapid calculation of the understanding of combinatorial optimization problems Point 2: Parallel processing possible on diverse hardware Point 3: Achieving low latency and secure environments ＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝ ■Flow of applying combinatorial optimization solutions for embedded systems 1. Problem Definition Clarify the problem domain and the objectives of optimization, and determine the direction for solution and modeling. 2. Mathematical Modeling Represent real-world problems and phenomena in mathematical equations or tables to make them predictable. 3. Definition of Objective Function Define the "objective function" that seeks the evaluation value when each variable is given. Define the acceptable "constraints" for each variable. 4. Combinatorial Optimization Calculate the optimal solution using a combinatorial optimization solver.
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■Application Example: Multi-Object Tracking in the Automotive Field In autonomous driving systems, a series of processes from sensing to recognition, judgment, and control are periodically realized. In the recognition process, Multi-Object Tracking (MOT) is used, and the results are input into judgment and control, leading to operations such as evasive maneuvers. By applying this technology to the matching process of objects between image frames, it becomes possible to associate detected objects across consecutive frames, enabling the prediction of moving object positions a few seconds ahead. This is considered effective for enhancing route planning in autonomous driving.
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We will provide optimal solutions that contribute to our customers' businesses through our "extensive experience and achievements accumulated over many years" and "high technical capabilities" in the fields of embedded and LSI design.
