Berkeley researchers have designed a methodology to solve in real-time linear programming (LP) problems with an analog circuit. Despite continued advancement of digital computers, the task of solving LP in very short times (e.g. 1 MHz for MPC based control of fast systems) remains challenging. Due to lack of temporal overlap between analog computation and MPC, there have been few investigations in applying analog computation towards MPC problems or LP problems. Using this technology, solution to real-time optimization problems can be achieved at 6 microseconds and ongoing work aims to reduce it to a few nanoseconds, which is lower than any current method known to our investigators.Possible applications of the new methodology are fast and power-efficient analog signal processing (e.g. Kalman filter), image processing (e.g. optical flow, mathematical morphology) and advanced control (e.g. model predictive control). Applications in automotive industry:The ability to find a solution for an optimization problem in fast and reliable manner serves well the need to design efficient and reliable vehicles. An analog optimization circuit, besides being faster than a digital counterpart, can be used in safety-critical systems, since it has a predictable and continuous behavior. The new circuit for analog optimization is significantly faster and simpler than previously known analog approaches. Therefore, this technology enables to design systems that are either faster or cheaper than the existing ones. The technology is broadly applicable in the automotive industry, since fast, reliable and power efficient embedded computing is required in many vehicle systems. Potential applications include the following fields: 1. Very fast Model Predictive Control (MPC) systems. 2. Low level image processing: Examples include optical flow or edge detection. 3. Signal processing embedded in the sensor e.g. Kalman filter.