Results from a neural-network trigger based on the digital MA16 chip of Siemens are reported. The neural trigger has been applied to data from the WA92 experiment, looking for beauty particles, which have been collected during a run in which a neural trigger module based on Intel's analog neural chip ETANN operated, as already reported. The MA16 board hosting the chip has a 16-bit I O precision and a 53-bit precision for internal calculations. It operated at 50 MHz, yielding a response time for a 16 input-variable net of 3 μs for a Fisher discriminant (1-layer net) and of 6 μs for a 2-layer net. Results are compared with those previously obtained with the ETANN trigger.

/pdf/results-from-a-ma16-based-neural-trigger-in-an-experiment-3l8pm7f46k.pdf

Results from a MA16-based neural trigger in an experiment looking for beauty

http://inspirehep.net/record/888386/files/thesis-98-016.pdf

The Hardware Track Finder Processor in CMS at CERN

A neural-network trigger has been developed for secondary vertex detection in the WA92 experiment at CERN, looking for the production of beauty particles. The module includes the Intel analog ETANN chip and the Siemens digital (16-bit I/O) MA 16 microprocessor, working independently. The experimental trigger results have already been presented elsewhere. Since this represents the first application of a neural trigger in an high energy physics experiment, we describe here in some detail its hardware and software structure. Specifics are given on the utilization of the ETANN chip, caring about the stability problems associated with its analog nature, and of the MA 16 chip, for which this also represents the first stand-alone application.

Hardware and software structure of a neural network trigger based on the ETANN and MA16 chips

The NeuroLution system consists of hardware and software components, which can be used as stand alone tools or in an integrated form. For many standard network types, simulators with comfortable graphical user interfaces are ooered. However, the system easily can be extended by user deened items. This exibility is achieved, because any simulator is characterized by two abstract deenitions: a network deenition written in the neural network description language CONNECT, and an interface deenition based on the GINA library of graphical user interface classes. Also, C++ classes generated from the CONNECT deenitions can be exported for the development of custom applications. The NeuroLution PCI board contains up to four SAND neurochips, and accelerates feedforward, RBF, and Kohonen networks. The peak performance is 800 MCPS. Using the driver's C interface, the board can be exploited as a stand alone tool. However, the integrated NeuroLu-tion system also ooers graphical simulators, as well as the underlying C++ classes, for exploiting the board. Beside presenting the NeuroLution system , the paper discusses requirements on neural network simulation tools and gives a short overview of the state of the art.

NeuroLution: integrated hardware and software for the development of neural network applications

This paper describes a feasibility study for the design of the muon trigger track finder processor in the high-energy physics experiment compact muon solenoid (CMS), planned for 2005, at CERN. It covers the specification, proposed method, and a prototype implementation. Comparison between several other measurement methods and the proposed one are carried out. The task of the processor is to identify muons and measure their transverse momenta and locations within 350 ns. It uses data from almost 200000 detector cells of drift tube muon chambers. The processor searches for muon tracks originating from the Interaction point by joining the track segments provided by the drift tube muon chamber electronics to full tracks. It assigns transverse momentum to each reconstructed track using the track's bend angle.

/pdf/feasibility-of-the-hardware-muon-trigger-track-finder-353nc8ee3i.pdf

Feasibility of the hardware muon trigger track finder processor in CMS

Results from a neural-network trigger based on the digital MA16 chip of Siemens are reported. The neural trigger has been applied to data from the WA92 experiment, looking for beauty particles, which have been collected during a run in which a neural trigger module based on Intel’s analog neural chip ETANN operated, as already reported. The MA16 has a precision of 16 bits for input variables, 16 bits for weights, 16 bits for scalar multipliers modifying the transfer function shape, 47 bits for thresholds, 53 bits for internal calculations, 38 bits for the output. Of the latter only 16 bits are used in the MA16 board, as input to the transfer function inplemented on 16-bit addressable EPROM’s. The MA16 board operated at 50 MHz, yielding a response time for a 16 input variable net of 3 μs for a Fisher discriminant (1-layer net) and of 6 μs for a 2-layer net. Results are compared with those previously obtained with the ETANN trigger.

/pdf/results-from-a-neural-trigger-based-on-the-ma16-pg6mh178kv.pdf

Results from a neural trigger based on the ma16 microprocessor

A neural trigger module for high-energy physics experiments has been realized. It employs the analog neural microprocessor ETANN by Intel and the 16-bit digital microprocessor MA16 by Siemens. With suitable I/O interfacing it has operated in the experiment WA92 at CERN, looking for beauty particles produced by a negative pion beam on a copper target, where it is used to accept events for recording on a special data stream to be analyzed with high priority. The response times of both the ETANN and the MA16 parts of the neural trigger, which act independently, are less than 10 microseconds. Results for the ETANN trigger are presented. >

http://ieeexplore.ieee.org/iel2/3013/8561/00374814.pdf

Application of neural microprocessors to high-energy physics experiments

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Results from a neural trigger based on the ma16 microprocessor

Application of neural microprocessors to high-energy physics experiments