With the development of smartphones, IoT and complex automated systems such as autonomous vehicles, there are more and more opportunities to integrate sensor-driven machine learning and optimization into embedded systems.
These technologies are widely used in AI, but why exactly use a nonlinear solver into embedded systems?
The solver may be used during the training phase of a Machine Learning model
For instance, a vision system that detects defects on a production line may have learnt from a set of data comprised of both compliant and faulty products. In practice this training involves the adjustment of many parameters in order to minimize the error rate. This often means solving a nonlinear optimization problem.
During the training phase of the model, many parameters are chosen in order to minimize a nonlinear objective function comprised of both the error and a regularization term.
In many cases, the training phase is carried out in the cloud, and the trained model is used to perform inference on the device. However, this process is slow because of the time needed to transmit the data and model updates between the device and the server. In situations where the device is expected to learn and respond to the new scenario instantly, there will be a need for local model updates. Local training also addresses the important issue of privacy, as the data does not need to leave the device.
Some applications require to perform real-time numerical optimization
Self-driving cars integrate machine learning and optimization in a complimentary way. The first enable the system to learn how to identify static and moving objects, as well as signalization.
Then the vehicle must compute a trajectory towards the desired location that maximizes safety and comfort while minimizing energy consumption and travel time, taking into account obstacles, signalization, road shape… This is a nonlinear optimization problem. And there are many more examples of embedded optimization such as drone stabilization or rocket landing!
Knitro 12.2 is available on demand for ARM processors (ARMv7 (32 bits) and ARMv8 (64 bits)). Moreover, for the enthusiasts, Knitro is available for Raspberry Pi (ARMv7, Raspbian 9).
— We are pleased to announce that Artelys Knitro 14.0 is now available! This new version enables compagnies to solve complex non-linear optimization problems with unprecedented efficency and precision.
Artelys to participate in the demonstration of digital twins for optimal grid and market actions in Europe
— Artelys is involved in the three-year Horizon Europe project TwinEU, which aims to create a digital twin of the entire European electricity grid with a unique consortium bringing together grid and market operators, research entities and technology providers.
— Planning for the energy transition requires the ability to optimize energy system development pathways, considering complex interactions and constraints, particularly concerning interactions between sectors and vectors. In this context, Artelys has developed a new method for solving these large-scale mathematical problems.
— The stakes of Artificial Intelligence (AI) have become a strategic issue for companies, regardless of their field of activity or size. As an expert, Artelys provides you with full assistance, from the design stage to the implementation of your first AI solution, by building with and for your team a roadmap tailored to your business.
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