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FPGA based particle identification in high energy physics experiments

Title	FPGA based particle identification in high energy physics experiments
Author	Uğurdağ, Hasan Fatih, Başaran, A., Akdogan, T., Güney, V. U., Gören, S.
Publication Date:	2012
Publication Place	- IEEE
Subject	Curve fitting, Field programmable gate arrays, Parallel processing, Particle detectors, Signal processing
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-4673-2243-0
Record ID	29289038-de88-4cd4-98c4-f908ee4014a0
Library Location	Electrical & Electronics Engineering
Date	2012
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	High energy physics experiments require on-the-fly processing of signals from many particle detectors. Such signals contain a high and fluctuating rate of pulses. Pulse shape hints particle type, and the amplitude relates to energy of the particle, while pulse occurrence times are needed for event reconstruction. Traditionally, these parameters have been extracted with the help of complete racks of dedicated electronics. Our FPGA design on a general-purpose DAQ card does real-time pulse detection and high-precision curve fitting. It greatly shrinks required equipment in terms of form factor, cost, power usage, and setup time. Unlike traditional systems, we can handle bursts of back-to-back pulses, pulses as narrow as 6 ns and at rates over 1M pulses per second. We have a novel scalable architecture that combines pipelining and parallelism. Moreover, the parallel part of the architecture uses loop pipelining in each of its interleaved identical parallel processors (IIPPs). An IIPP is a specialized CPU, which executes nested loops, with number of iterations that varies from pulse to pulse. IIPPs are fed data from a FIFO by a priority encoder based dispatcher. Number of IIPPs can be calculated to meet any pulse rate and average pulse width. The architecture is flexible enough to work with a variety of curve fitting algorithms.
DOI	10.1109/ASAP.2012.22

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Özyeğin University

FPGA based particle identification in high energy physics experiments

Author Uğurdağ, Hasan Fatih, Başaran, A., Akdogan, T., Güney, V. U., Gören, S.

Publication Date 2012

Publication Place - IEEE

Subject Curve fitting, Field programmable gate arrays, Parallel processing, Particle detectors, Signal processing

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-4673-2243-0

Record ID 29289038-de88-4cd4-98c4-f908ee4014a0

Library Location Electrical & Electronics Engineering

Date 2012

Notes Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Sample Text High energy physics experiments require on-the-fly processing of signals from many particle detectors. Such signals contain a high and fluctuating rate of pulses. Pulse shape hints particle type, and the amplitude relates to energy of the particle, while pulse occurrence times are needed for event reconstruction. Traditionally, these parameters have been extracted with the help of complete racks of dedicated electronics. Our FPGA design on a general-purpose DAQ card does real-time pulse detection and high-precision curve fitting. It greatly shrinks required equipment in terms of form factor, cost, power usage, and setup time. Unlike traditional systems, we can handle bursts of back-to-back pulses, pulses as narrow as 6 ns and at rates over 1M pulses per second. We have a novel scalable architecture that combines pipelining and parallelism. Moreover, the parallel part of the architecture uses loop pipelining in each of its interleaved identical parallel processors (IIPPs). An IIPP is a specialized CPU, which executes nested loops, with number of iterations that varies from pulse to pulse. IIPPs are fed data from a FIFO by a priority encoder based dispatcher. Number of IIPPs can be calculated to meet any pulse rate and average pulse width. The architecture is flexible enough to work with a variety of curve fitting algorithms.

DOI 10.1109/ASAP.2012.22