1.WHAT CERN NEEDS TO DO IN COLLISIONS?
When collecting data from collisions, CERN also needs fast and efficient decision-making when analyzing millions of particle collisions produced by the Large Hadron Collider (LHC) detector. His unique data analysis skills have led CERN and his Zenseact to work together to study how to apply high energy physics organization machine learning techniques to the field of autonomous driving.
2.HOW CERN DOING GOOD?
So-called Field Programmable Gate Arrays (FPGAs) were chosen as a hardware benchmark for handling computer vision tasks. FPGAs, long used at CERN, are configurable integrated circuits that can execute complex decision-making algorithms in microseconds.
3.WHICH WILL RESEARCHERS NEEDS TO DO?
Researchers have found that by optimizing existing resources, far more functionality can be packed into FPGAs. Best of all, even processing units with limited computing resources were able to perform tasks with high precision and low latency.
Our joint work described FPGA compression techniques that could also have a significant impact on improving processing efficiency in the LHC data center. Future developments in this area of research could make important contributions to several other areas beyond high-energy physics, as machine learning platforms set the stage for next-generation solutions. It can also be used to improve the efficiency of algorithms while maintaining accuracy in areas ranging from improving energy efficiency in data centers to cell screening for medical applications.