Golana Computing, a new startup, is pioneering a new magnetization reversal scheme through the MAG.Net project. This innovative approach uses domain wall (DW) depinning from geometrical chambers to mimic the behavior of biological spiking neurons. This breakthrough enables the design and fabrication of bio-mimicking magnetic neurons capable of recognizing analog signals without the typical requirement of feature extraction.
Our initial work on magnetic spiking neurons began with a proof of concept that demonstrated real-time speech recognition and speaker identification. Following this success, we secured EIC Transition funding in May 2023 under the theme "Green Digital Devices." This European grant has allowed us to advance the development of our innovative magnetic neuronal system.
Our goal is to develop an innovative system for predictive maintenance. Manufacturing industries' maintenance departments need predictive solutions to prevent downtimes, unpredictable or high costs, catastrophic failures, and address production or quality issues. They require a straightforward solution tailored to their machines. This is especially true in the case of SMEs, which lack the data scientists and ML/AI experts able to build such applications. In fact, most manufacturing facilities do not have technological solutions for maintenance or dedicated departments to advance towards Industry 4.0. Our system aims to fill this gap by providing an accessible and effective predictive maintenance solution to enhance operational efficiency and reliability.
The MAG.Net project allows us to develop a technology and create a prototype device with a unique aptitude for predictive maintenance in the manufacturing industry. Indeed, the diversity of different machines and processes encountered in the manufacturing industry, requires a solution that is extremely versatile, based on a device that can be “task agnostic”. In that purpose, our magnetic neuronal network has already demonstrated its ability to process different analog signals on equal footing. Moreover, by operating on the edge, it will reduce waste and enable energy savings, which is particularly crucial in today’s context. This system will also minimize the resources required for data transmission, computation, and security. By the end of the project, we aim to extend this technology to the largest possible range of applications, increasing competitiveness and promoting production relocation to Europe across multiple industries.
Our long term goal is to extend even further the versatility of our solution, for other applications requiring low energy consumption and full privacy. By expanding the application field beyond the analog signals from industrial machines, we will be able to address the needs of other sectors that require innovative, agnostic edge solutions.
By the end of the first year of the MAG.Net project, we have consolidated a large database of analog signals from a diverse set of machines, based on which we have developed our technology. Our first prototype is already capable of detecting errors, failures, anomalous behaviors, and drifts in industrial machines.
