Accelerating combinatorial optimization in embedded devices | Quantum-inspired optimization
Support for real-time decision-making | Embedded combinatorial optimization solutions
The combinatorial optimization problems in embedded systems are important issues that also affect control accuracy. Toshiba Information Systems provides combinatorial optimization solutions for embedded systems utilizing Toshiba's unique quantum-inspired optimization technology. We quickly compute good solutions to combinatorial optimization problems and support parallel processing on diverse hardware. Furthermore, by processing in an embedded environment, we assist in achieving a low-latency and secure environment. ■ Do you have these challenges? - Want to process combinatorial optimization problems quickly - Want to perform optimization processing with limited resources of embedded devices - Face strict hardware constraints and cannot achieve sufficient performance - Want to realize processing that leads to real-time decision-making and control - Want to process without relying on an online connection environment - Want to achieve a low-latency processing environment in embedded systems - Want to perform optimization processing in a secure environment - Considering optimization processing using FPGAs and other technologies ■ We propose our combinatorial optimization solutions for embedded systems to address these challenges! ✓ Quickly compute good solutions to combinatorial optimization problems ✓ Capable of parallel processing on diverse hardware ✓ Achieve a low-latency and secure environment
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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 fast and secure combinatorial optimization solution using Toshiba's unique quantum-inspired optimization technology for combinatorial optimization problems. ＝＝＝＝＝＝＝＝＝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. Definition of the problem Clarify the problem domain and the objectives of optimization, and determine the direction for solutions and modeling. 2. Mathematical modeling Represent real-world problems and phenomena in mathematical equations or tables to make them predictable. 3. Definition of the objective function Define the "objective function" that seeks the evaluation value when each variable is given. Define the "constraints" that are acceptable 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 realized periodically. In the recognition process, Multi-Object Tracking (MOT) is used, and the results are input into the judgment and control, leading to actions 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.
