Posted by TORcicada 11 hours ago
Some slides with more info: https://indico.cern.ch/event/1496673/contributions/6637931/a... The approval process for a full paper is quite lengthy in the collaboration, but a more comprehensive one is coming in the following months, if everything went smoothly.
Regarding the exact algorithm: there are a few versions of the models deployed. Before v4 (when this article was written), they are slides 9-10. The model was trained as a plain VAE that is essentially a small MLP. In inference time, the decoder was stripped and the mu^2 term from the KL div was used as the loss (contributions from terms containing sigma was found to be having negliable impact on signal efficiency). In v5 we added a VICREG block before that and used the reconstruction loss instead. Everything runs in =2 clock cycles at 40MHz clock. Since v5, hls4ml-da4ml flow (https://arxiv.org/abs/2512.01463, https://arxiv.org/abs/2507.04535) was used for putting the model on FPGAs.
For CICADA, the models was trained as a VAE again, but this time distilled with supervised loss on the anomaly score on a calibration dataset. Some slides: https://indico.global/event/8004/contributions/72149/attachm... (not up-to-date, but don't know if there other newer open ones). Both student and teacher was a conventional conv-dense models, can be found in slides 14-15.
Just sell some of my works for running qat (high-granularity quantization) and doing deployment (distributed arithmetic) of NNs in the context of such applications (i.e., FPGA deployment for <1us latency), if you are interested: https://arxiv.org/abs/2405.00645 https://arxiv.org/abs/2507.04535
Happy to take any questions.
I have since pivoted a lot of my PhD work (still related the HLS and EDA). But I wonder what is the current main limitation/challenges of building these trigger systems in hardware today. For example, in my mind it seems like the EDA and tooling can be a big limitation such as reliance on commercial HLS tools which can be buggy, hard to use, and hard to debug. From experience, this makes it harder to build different optimized architectures in hardware or build co-design frameworks without having high HLS expertise or putting in a lot of extra engineering/tooling effort. Also tool runtimes make the design and debug cycle longer, especially if you are trying to DSE on post-implementation metrics since you bring in implementation tools as well.
But I might be way off here and the real challenges are with other aspects beyond the tools.
https://arxiv.org/html/2411.19506v1
Why is it so hard to elaborate what AI algorithm / technique they integrate? Would have made this article much better
Already the case with consulting companies, have seen it myself
I do know about linear regression even had quite some of it at university.
But I still wouldn’t be able to just implement it on some data without good couple days to weeks of figuring things out and which tools to use so I don’t implement it from scratch.
let v0 = 0
let v1 = 0.40978399*(0.616*u + 0.291*v)
let v2 = if 0 > v1 then 0 else v1
let v3 = 0
let v4 = 0.377928*(0.261*u + 0.468*v)
let v5 = if 0 > v4 then 0 else v4... // inputs: u, v
// --- hidden layer 1 (3 neurons) ---
let v0 = 0.616*u + 0.291*v - 0.135
let v1 = if 0 > v0 then 0 else v0
let v2 = -0.482*u + 0.735*v + 0.044
let v3 = if 0 > v2 then 0 else v2
let v4 = 0.261*u - 0.553*v + 0.310
let v5 = if 0 > v4 then 0 else v4
// --- hidden layer 2 (2 neurons) ---
let v6 = 0.410*v1 - 0.378*v3 + 0.528*v5 + 0.091
let v7 = if 0 > v6 then 0 else v6
let v8 = -0.194*v1 + 0.617*v3 - 0.291*v5 - 0.058
let v9 = if 0 > v8 then 0 else v8
// --- output layer (binary classification) ---
let v10 = 0.739*v7 - 0.415*v9 + 0.022
// sigmoid squashing v10 into the range (0, 1)
let out = 1 / (1 + exp(-v10))From around when the term was first coined: "artificial intelligence research is concerned with constructing machines (usually programs for general-purpose computers) which exhibit behavior such that, if it were observed in human activity, we would deign to label the behavior 'intelligent.'" [1]
At some point someone will realise that backpropagation and adjoint solves are the same thing.
[1] https://archive.ics.uci.edu/ml/datasets/HIGGS
In my experiments, linear regression with extended (addition of squared values) attributes is very much competitive in accuracy terms with reported MLP accuracy.
https://opendata-qa.cern.ch/record/93940
if you can beat it with linear regression we'd be happy to know.
It’s impressive, honestly.
https://news.ycombinator.com/item?id=12340348 Neural network spotted deep inside Samsung's Galaxy S7 silicon brain (2016)
https://ieeexplore.ieee.org/document/831066 Towards a high performance neural branch predictor (1999)
It's not about linear algebra (which is just used as a way to represent arbitrary functions), it's about data. When your problem is better specified from data than from first principles, it's time to use an ML model.
And for historians: Delphi people (amongst others) had papers on Higgs selection using (A)NN from LEP data (overfit :) , obviously without the 5 sigma. It was an argument for LHC.
Dear downvoters/shadowbanners: do your homework.
https://www.youtube.com/watch?v=8IZwhbsjhvE (From Zettabytes to a Few Precious Events: Nanosecond AI at the Large Hadron Collider by Thea Aarrestad)
Page: https://www.scylladb.com/tech-talk/from-zettabytes-to-a-few-...
(Probably not for this here though.)
- FPAGs like this one are generally COTS.
- All the experiments use GPUs which come straight from the vendors.
- Most of the computing isn't even on site, it's distributed around the world in various computing centers. Yes they also overflow into cloud computing but various publicly funded datacenters tend to be cheaper (or effectively "free" because they were allocated to CERN experiments).
Some very specific elements (those in the detector) need to be radiation hard and need O(microsecond) latency. These custom electronics are built all over the world by contributing national labs and universities.
CERN builds a bit.
It is a project bureau. Everything is essentially outsourced, leaving a management shell institute to parade for VIPs. Actually they are close to completely forgetting what they already knew in the hard sciences domain.
Who says CERN needs to be cost effective?
I was just answering this question. LLM logic in weights is fundamentally from machine learning, so yes. Wasn't really saying anything about the article.
Much of the early AI research was spent on developing various algorithms that could play board games.
Didn't even need computers, one early AI was MENACE [1], a set of 304 matchboxes which could learn how to play noughts and crosses.
[1] https://en.wikipedia.org/wiki/Matchbox_Educable_Noughts_and_...
I didn't know what a Jujube was, but I got the idea.
So they aren't "burned into silicon" then? The article mentions FPGAs and ASICs but it's a bit vague. I would be surprised if ASICs actually made sense here.
> To meet these extreme requirements, CERN has deliberately moved away from conventional GPU or TPU-based artificial intelligence architectures.
This isn't quite right either: CERN is using more GPUs than ever. The data processing has quite a few steps and physicists are more than happy to just buy COTS GPUs and CPUs when they work.