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Electrical Engineering at Fermilab

Electrical Engineering at Fermilab. The Hidden Agenda Behind All This Physics Stuff. Presented by:. Jim Hoff and Farah Fahim. (Jim got too much credit on the poster). Engineers build machines.

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Electrical Engineering at Fermilab

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  1. Electrical Engineering at Fermilab The Hidden Agenda Behind All This Physics Stuff

  2. Presented by: Jim HoffandFarah Fahim (Jim got too much credit on the poster)

  3. 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. Engineering Engineering versus Physics…what’s the real difference? Physics Physicists pursue phenomena. In order to do so, if they have to build some machines, that is just the cost of business

  4. Electrical Engineering is MUCH better. Engineering is better That being said…

  5. 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… For the rest of the talk

  6. What does a Physicist see? Physicists pursue phenomenaso they SEE phenomena. They see particles and their interaction.

  7. Engineers see the machines. We see the hundreds and thousands of little detectors. What does an Engineer see?

  8. Engineers see the machines. We see the hundreds and thousands of little detectors. What does an Engineer see? 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. 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.

  9. Engineers see the machines. We see the hundreds and thousands of little detectors. What does an Engineer see?

  10. For example: LAr Detectors like LBNE 10000 electrons Pixel Detectors in CMS 1000 electrons CCD Detectors in CDMS A few electrons “Tiny Puffs of Charge”? Really?

  11. What do we do with these tiny puffs of charge?

  12. Geometry Size Neighborhood Time Power How do we get this done? Limitations

  13. Shut up and let Farah talk…

  14. Remember this?

  15. Every 25ns… Most of these events are meaningless, and the amount of information gathered is staggering, so we have to discard most of it. This is where we start… Still, when we find something interesting, we have to turn this…

  16. We have to extract the significant particles from the meaningless ones and from the noise. Into this…

  17. The desire for high momentum tracks allows us to narrow the scope to a set of towers How? Simulations prior to experimentation allow us to predict patterns of hit detectors that indicate a significant track amid all the noise.

  18. For simplicity, we will look at this in 2 dimensions rather than 3. Real Time Track Finding 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.

  19. 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. What is a Pattern Recognition Associative Memory?

  20. 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. What is a Pattern Recognition Associative Memory?

  21. Pattern Recognition 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.

  22. Road! Layer 3 Address 9 Layer 4 Address 4 Layer 1 Address 4 Layer 2 Address 1 Layer 1 Address 4 Layer 3 Address 7 Layer 2 Address 1 Layer 3 Address 9 Layer 3 Address 7 Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match High-Speed Pattern Recognition Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match Layer 4 Address 4 Layer 2 Address 4 Layer 2 Address 4 Match Match Match Match Match Match Match Match Match Match Match Match Match Match Match

  23. 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

  24. 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.

  25. Final 3D Implementation

  26. 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. Conclusions

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