1. Overview of the ATLAS Fast Tracker (FTK) (daughter of the very successful CDF SVT)
July 24, 2008
M. Shochet

2. What is it for? At the LHC design accelerator intensity:
New phenomena:  0.05 Hz
Total interaction rate:  1 GHz (40 MHz beam crossings)
Many possible new phenomena produce heavy b quarks which can only be distinguished from the bulk of the background by reconstructing the individual tracks.
We are proposing to significantly enhance the ability of ATLAS to rapidly identify b quarks in the trigger.
Currently done in commodity PC’s. This is slow and becomes slower as the accelerator intensity and thus track density increase.
The problem!
beam pipe
few mm
July 24, 2008
M. Shochet

3. ATLAS
July 24, 2008
M. Shochet

4. Inner Tracking Detectors
Pixels barrel
SCT barrel
Pixels disks
3 pixel layers (space point)
8 strip layers (1 coordinate)
 11 layers, 14 coordinates
July 24, 2008
M. Shochet

5. Getting data into FTK on L1 accept RODs SCT  Pixels  FTK
dual-output HOLA designed by Tang
ROBs
ROBs
silicon hits
silicon tracks
ask for ROI’s
Level 2
July 24, 2008
M. Shochet

6. Number of input fibers Number of crates
Pixels: 120
Strips: 92
Number of crates
 12
July 24, 2008
M. Shochet

7. How does it work?
First do pattern recognition, then fit possible candidates.
Prestore patterns (roads) in large content-addressable memory.
coarse resolution hits
full resolution hits
(superbins)
The Pattern Bank
(4-layer)
July 24, 2008
M. Shochet

8. Massively parallel pattern recognition
track fitter
1 superbin per silicon layer
Majority logic allows up to 1 missed layer.
July 24, 2008
M. Shochet

9. Functional layout RODs ~Offline quality Track parameters Pixels & SCT
PIPELINED AM
overlap
regions
RODs
EVENT # 1
EVENT # N
50~100 KHz
event rate
HITS
(LVDS links)
AM-board
Data
Formatter
(DF)
DO-board
S-links
SUPER BINS
DATA
ORGANIZER
ROADS
cluster finding
split by layer
ROADS + HITS
Track Fitter
~Offline quality
Track parameters
Raw data
ROBINs
Track data
ROBIN
July 24, 2008
M. Shochet

10. Possible layout for a core crate (after DFs)
AM-B7
AM-B8
AM-B1
AM-B0
DO5
DO4
DO3
DO2
DO1
DO0
Track Fitter
AM-B2
AM-B3
AM-B4
AM-B5
AM-B6
AM-B9
AM-B10
AM-B11
AM-B12
July 24, 2008
M. Shochet

11. Data Formatter Receives raw hits from the detector (RODs)
Finds hit clusters
pixels silicon strips
Store cluster centroids
Separates clusters by silicon layer & sends to Data Organizers on 6 LVDS data busses (22 bits each)
July 24, 2008
M. Shochet

12. Data Organizer Receives hits from Data Formatters.
Stores hits at full resolution in a way that is rapidly accessible by pattern number.
Sends hits at coarser resolution (superbin) to pattern recognition unit (Associative Memory).
Receives patterns from AM, retrieves full resolution hits, and sends road number and hits to the Track Fitter.
July 24, 2008
M. Shochet

13. Track Fitter Receives road # and associated hits from Data Organizers.
Computes all hit combinations
Calculate the track parameters
curvature, azimuthal angle, polar angle, z0, impact parameter
and the goodness of fit (2) using a linear correction to the mean for that sector (excellent precision over a sector).
sector: a physical silicon module in each layer
pixels: 1" x 2.5 "
strips: 2.5" x 5 "
July 24, 2008
M. Shochet

14. Output good tracks to ROBIN.
14 measurements, 5 parameters  9 constraints (2)
Pi: 5 track parameters & 9 constraints (2 is sum of squares)
xj: the hit coordinate in layer j
aij, bi: the stored constants for each sector, calculated in advance from a large sample of training tracks (simulation or data)
Cut on goodness of fit; among the combinations in a road, select the track with the best 2.
Output good tracks to ROBIN.
July 24, 2008
M. Shochet

15. Readout Buffer (ROBIN)
Stores tracks for access by the level-2 trigger PCs.
July 24, 2008
M. Shochet

16. Track Fitter details
GigaFitter – a simplified version built for the CDF SVT
2D reconstruction (transverse to the beamline)
6 detector layers
3 track parameters (curvature, azimuthal angle, impact parameter)
3 constraints  2
July 24, 2008
M. Shochet

17. GigaFitter scheme Comb - FiFo
Lay0-Ram
Lay1- Ram
Lay2- Ram
Lay3-Ram
...
Lay10-Ram
Comb - FiFo
DSP:
Fit Tracks
Choose best χ2 track
INPUT FiFo
Constants RAM
DO roads & hits  input FIFO  RAMs according to detector layer
Combinations (one hit/layer) calculated sequentially & stored in Comb-FIFO
Each combination & the constants are sent to DSP for fitting & selection
July 24, 2008
M. Shochet

18. DSP algorithm for SVT C1 18 39
ACC 39
FIFO
Hit 18 C2 18 39
ACC 39
156 18 39
ACC C3 39 39
ACC 18 39 C4
constants
RST
DSP48E
CTRL EV
READY
RST
Comb-FIFO data serialized: 1 hit and its constants sent to parallel DSP slices each computing a track parameter or constraint.
An additional DSP computes total 2 from individual constraints
Total of 7 DSP slices in parallel each working at 200 MHz
July 24, 2008
M. Shochet

19. DSP Slice 18 39
One DSP slice computes a track parameter in 14 clock cycles, plus 4 for the first one.
July 24, 2008
M. Shochet

20. Xilinx XC5VSX95T 14720 V5 slices (4 LUT + 2 FF)
1% used for each fitter
1520 kbit of distributed RAM
640 DSPs
2.5% used for each fitter in the FTK version
5 parameters, 9 constraints, 1 to calculate overall 2
  40 fitters/chip (remember: many combinations per road)
1 Mbyte of block RAM
plenty for the SVT prototype
not even close for the FTK!!
July 24, 2008
M. Shochet

21. Missing hit problem
To obtain high reconstruction efficiency, we must allow one physical detector layer to miss a hit.
The constants in the parameter and constraint equations are different when there is a missing hit.
Store 12 sets of constants (all hits, a miss in one of 11 layers).
A lot of memory that has to be accessed very quickly. Can one look ahead for the constant set that will be needed next?
Alternative is to estimate the hit location in the missing layer.
How long does it take?
July 24, 2008
M. Shochet

22. Size of the constant memory
210 words/constant set (14 )
If we need 2-byte precision  420 bytes/constant set
One constant set/sector. Currently estimate 100k sectors in an FTK crate.
 42 Mbytes of fast memory
 0.5 Gbyte if solve missing hit problem with more memory
We have heard that with the latest FPGAs, there is very fast access to external computer-like memory. Is that true?
July 24, 2008
M. Shochet

23. How many fitters are needed?
The number of cycles from the time the data from a road is in the input FIFO until the track parameters are in the output FIFO is approximately:
NfitCycl = Ncomb  Nhits (all 14 calculations done in parallel)
A road packet takes Nhits + 1 Data Organizer clock cycles to be sent to the Track Fitter. Thus if we are to have 0 deadtime from track fitting, we need the number of parallel fitters (there are 40 in a chip), Nfitters, satisfying:
This translates into a maximum average Ncomb of
Nfitters  ClockRatio  (Nhits + 1)/Nhits
where ClockRatio is the ratio of the fitter to DO clock speeds.
We will have to satisfy this: road width, # of FPGAs.
July 24, 2008
M. Shochet

24. Hit Warrior function
One can easily get many tracks (ghosts) from a single real particle due to presence of extra random silicon hits.
Compare a new track with those already found. If new one has 8 or more hits in common with a stored track, keep only the best 2 track.
If space permits, add this function to the Track Fitter.
July 24, 2008
M. Shochet

25. Spy Buffer
Data flows very quickly through this system. By the time any PC using its output detects a problem, the data would already be long gone from the FTK. That makes diagnosing a problem that is occurring internally in the FTK extremely difficult.
We found it very useful in the SVT to have deep buffers at the input and output of every board in the system. Then, when a problem is detected, these spy buffers can be frozen in the entire system and read out. The buffer is deep enough so the event with the error is still inside it.
This allows diagnosing and fixing subtle problems.
July 24, 2008
M. Shochet