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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Annovi, A. Amerio, S. Beretta, M. Bossini, E. Crescioli, F. Dell'Orso, M. Giannetti, P. Hoff, J. Liu, T. Magalotti, D. Piendibene, M. Sacco, I. Schoening, A. Soltveit, H. Stabile, A. Tripiccione, R. Liberali, V. Vitillo, R. Volpi, G. |
| Copyright Year | 2011 |
| Description | Author affiliation: INFN Padova (Amerio, S.) || INFN Perugia (Magalotti, D.) || INFN Ferrara (Tripiccione, R.) || INFN Pisa (Bossini, E.; Crescioli, F.; Dell'Orso, M.; Giannetti, P.; Piendibene, M.; Sacco, I.; Vitillo, R.) || INFN Frascati (Annovi, A.; Beretta, M.; Volpi, G.) || University of Heidelberg (Schoening, A.; Soltveit, H.) || INFN Milano (Stabile, A.; Liberali, V.) || Fermilab (Hoff, J.; Liu, T.) |
| Abstract | We describe an important advancement for the Associative Memory device (AM). The AM is a VLSI processor for pattern recognition based on Content Addressable Memory (CAM) architecture. The AM is optimized for on-line track finding in high-energy physics experiments. Pattern matching is carried out by finding track candidates in coarse resolution “roads”. A large AM bank stores all trajectories of interest, called “patterns”, for a given detector resolution. The AM extracts roads compatible with a given event during detector read-out. Two important variables characterize the quality of the AM bank: its “coverage” and the level of fake roads. The coverage, which describes the geometric efficiency of a bank, is defined as the fraction of tracks that match at least one pattern in the bank. Given a certain road size, the coverage of the bank can be increased just adding patterns to the bank, while the number of fakes unfortunately is roughly proportional to the number of patterns in the bank. Moreover, as the luminosity increases, the fake rate increases rapidly because of the increased silicon occupancy. To counter that, we must reduce the width of our roads. If we decrease the road width using the current technology, the system will become very large and extremely expensive. We propose an elegant solution to this problem: the “variable resolution patterns”. Each pattern and each detector layer within a pattern will be able to use the optimal width, but we will use a “don't care” feature (inspired from ternary CAMs) to increase the width when that is more appropriate. In other words we can use patterns of variable shape. As a result we reduce the number of fake roads, while keeping the efficiency high and avoiding excessive bank size due to the reduced width. We describe the idea, the implementation in the new AM design and the implementation of the algorithm in the simulation. Finally we show the effectiveness of the “variable resolution patterns” idea using simulated high occupancy events in the ATLAS detector. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 649642 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457709258 |
| e-ISBN | 9781457709272 |
| DOI | 10.1109/ANIMMA.2011.6172856 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-06 |
| Publisher Place | Belgium |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Roads Detectors Hardware Silicon Spatial resolution Pattern matching |
| Content Type | Text |
| Resource Type | Article |
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