Programmable active memories in real-time tasks: implementing data-driven triggers for LHC experiments

The future Large Hadron Collider (LHC), to be built at CERN, presents among other technological challenges a formidable problem of real-time data analysis. At a primary event rate of 40 MHz, a multi-stage trigger system has to analyze data to decide which is the fraction of events that should be preserved on permanent storage for further analysis. We report on implementations of local algorithms for feature extraction as part of triggering, using the detectors of the proposed ATLAS experiment as a model. The algorithms were implemented for a decision frequency of 100 kHz, on different data-driven programmable devices based on structures of field- programmable gate arrays and memories. The implementations were demonstrated at full speed with emulated input, and were also integrated into a prototype detector running in a test beam at CERN, in June 1994

A Neural Network for Global Second Level Trigger - A Real-time Implementation on DecPeRLe-1

In the second level triggering for ATLAS "Regions of Interest" (RoIs) are defined in (etha, phi) corresponding to possibly interesting particles. For every RoI physically meaningful parameters are extracted for each subdetector. Based on these parameters a classification of the particle type is made. A feed-forward neural net with 12 input variables, a 6-node intermediate layer, and 4 output nodes has earlier been suggested for this classification task. The reported work consists of an implementation of this neural net using a DECPeRLe-1, a Programmable Active Memory (PAM). This is a reconfigurable processor based on Field Programmable Gate Arrays (FPGAs), which has also been used for real-time implementation of feature extraction algorithms for second level triggering. The implementation is pipelined, runs with a clock of 25 MHz, and uses 0.64 microseconds for one particle classification. Integer arithmetic is used, and the performance is comparable to a floating point version. 1 Intr..

Physiological and ecological significance of biological ice nucleators.

When a pure water sample is cooled it can remain in the liquid state at temperatures well below its melting point (0 degrees C). The initiation of the transition from the liquid state to ice is called nucleation. Substances that facilitate this transition so that it takes place at a relatively high sub-zero temperature are called ice nucleators. Many living organisms produce ice nucleators. In some cases, plausible reasons for their production have been suggested. In bacteria, they could induce frost damage to their hosts, giving the bacteria access to nutrients. In freeze-tolerant animals, it has been suggested that ice nucleators help to control the ice formation so that it is tolerable to the animal. Such ice nucleators can be called adaptive ice nucleators. There are, however, also examples of ice nucleators in living organisms where the adaptive value is difficult to understand. These ice nucleators might be structures with functions other than facilitating ice formation. These structures might be called incidental ice nucleators