Electrical Engineering
at Fermilab
The Hidden Agenda Behind
All This Physics Stuff
Jim Hoffand
Farah Fahim
(Jim got too much credit on the poster)
Presented by:
Engi
neer
ing
vers
us
Phys
ics…
what
’s th
e re
al d
iffer
ence
?Engineerin
g
Physics
Engineers build machines.If, along the way, they happen to uncover some phenomena or help other people to do so…oh well, that was fun.
Physicists pursue phenomena.In order to do so, if they have to build some machines, that is just the cost of business
That
bei
ng
said
…
Engineering is
better
Electrical Engineering
is MUCH better.
For t
he re
st o
f th
e ta
lk
There are lots of types of electrical
engineering at Fermilab… Power Engineering RF Engineering Board Design Etc…etc…etc…For the remainder of the talk, we’ll focus
on Front End Electronics and Integrated
Circuit Design Engineering, largely
because it is widely regarded as the best,
most significant and most interesting
type of electrical engineering, but also
because it is what we do.No one said we couldn’t be biased in this
presentation…
Wha
t doe
s a
Phys
icist
see?
Physicists pursue phenomena so they SEE phenomena. They see particles and their interaction.
Wha
t doe
s an
Engi
neer
see?
Engineers see the machines. We see the hundreds and thousands of little detectors.
Wha
t doe
s an
Engi
neer
see?
Engineers see the machines. We see the hundreds and thousands of little detectors.
We see tiny puffs of charge that “magically” appear at the inputs of our electronics. On some level we really don’t care where they come from.
It is also significant that, at least at first, there is NO ORDER to what we find and there can be a LOT of noise. Order must be extracted and noise must be suppressed.
Wha
t doe
s an
Engi
neer
see?
Engineers see the machines. We see the hundreds and thousands of little detectors.
“Tin
y Pu
ffs o
f Ch
arge
”? R
eally
? For example:LAr Detectors like LBNE10000 electrons
Pixel Detectors in CMS1000 electronsCCD Detectors in CDMS A few electrons
Wha
t do
we d
o wi
th
thes
e tin
y pu
ffs o
f ch
arge
?
Limita
tions
GeometrySizeNeighborhoodTime
Power
How do we get this done?
Shut
up
and
let
Fara
h ta
lk…
Rem
embe
r thi
s?
This
is wh
ere
we
star
t…
Every 25ns…Most of these events are meaningless, and the amount of information gathered is staggering, so we have to discard most of it.
Still, when we find something interesting, we have to turn this…
Into
this…
We have to extract the significant particles from the meaningless ones and from the noise.
How?
The desire for high momentum tracks allows us to narrow the scope to a set of towers
Simulations prior to experimentation allow us to predict patterns of hit detectors that indicate a significant track amid all the noise.
Real
Tim
e Tra
ck
Findi
ng
For simplicity, we will look at this in 2 dimensions rather than 3.
Layers correspond to, for example, each set of concentric cylinders within the tracking detector.Imagine simulating all conceivable tracks within this space and then recording those tracks in a Pattern Recognition Associative Memory.
Wha
t is a
Pat
tern
Re
cogn
ition
As
socia
tive
Mem
ory?
Ordinary read-only memories
respond to a new address
presented at its inputs with
the data corresponding to that
address. Someone gives it an
address and the ROM responds with data. Simple.
Associative Memories respond
to data with data.A single piece of data given to
an associative memory could
result in several associations
or it could result in none.
Wha
t is a
Pat
tern
Re
cogn
ition
As
socia
tive
Mem
ory?
Pattern Recognition Associative
Memories take it one step
further.Data is first subdivided into
categories. For example, hair
color, eye color, height and
weight.Data is only matched within
category. For example, hair
color data is only matched
against hair color patterns. Once a match is found in each
category, we have found a
potentially interesting pattern.
Patte
rn
Reco
gniti
on in
HEP
Our categories are detector layers.Our data are detector addresses within each detector layer.Given a pattern recognition associative memory with enough patterns to cover the tower and with the speed necessary to match patterns in the time allowed, we can do the job.
High
-Spe
ed P
atte
rn
Reco
gniti
onLayer 1
Address 4 Mat
chLayer 1Address 4 M
atch
Mat
ch
Layer 3Address 7
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ch
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chLayer 3Address 7
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ch
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ch
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ch
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chLayer 3Address 9
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ch
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ch
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ch
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chLayer 3Address 9
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ch
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ch
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ch
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chM
atch
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ch
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ch
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ch
Layer 2Address 1
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ch
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ch
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ch
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ch
Layer 2Address 1 M
atch
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ch
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ch
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ch
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ch
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ch
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ch
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ch
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ch
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ch
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ch
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ch
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ch
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ch
Layer 2Address 4 M
atch
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ch
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ch
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ch
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ch
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ch
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ch
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ch
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ch
Road!
The CAM Cell
• In fact, a direct implementation of the figure on the preceding page proved to be possible and it is shown here. To the left is a floorplan of the layout and to the right is the layout itself.
• This implementation brings out several features of the VIPRAM not immediately obvious. First, unlike the classical 2D PRAM architecture which is in a straight line, the resultant square layout of the 3D VIPRAM permits routing of signals from left, right, top and bottom. Second, the matchline of the CAM cell itself is shortened. In the TIPP paper, we talk about the shortening of the Stored Matchlines (Page 7, below Figure 4) and indicate that this will reduce power. Frankly, we were wrong. The Stored Matchlines do not change state rapidly, so they don’t draw much power. However, the CAM match lines run at 100+ MHz, and reducing their parasitic capacitance dramatically reduces the system power consumption.
• None of this was disclosed publicly at TIPP.
matchLine
The Control Cell (Majority Logic)
• A direct implementation of the Majority Logic as shown on Slide 9 is also possible. To the left is a floorplan of the layout and to the right is the layout itself.
Final
3D
Impl
emen
tatio
n
Conc
lusio
ns
Engineers build machines,
and the accelerators and detectors in HEP are among
the most complex machines
in history.In fact, these machines are
themselves composed of smaller machines that are,
each in their own right, enormously complex.
All joking aside, this place is
an engineer’s playground.