123 citations · 125 across the 9 of their papers we have counts for
13 papers
Alkaid: A Compiler Framework for Ultra-Low-Latency Kernels on Hardware
Chang Sun, Zhiqiang Que, Dimitrios Danopoulos +3
Ultra-low-latency machine learning and data processing pipelines operating on the sub-microsecond level often contain static dataflow kernels that require fine-grained bitwidth con…
Knowledge Distillation of a Normalising Flow for Real-Time Anomaly Detection at LHC Level-1 Trigger
Jaiman Abson, Florencia Canelli, Valentina Guglielmi +4
We present a new strategy for unsupervised anomaly detection at the hardware-based first stage of event processing (Level-1 trigger) of the Large Hadron Collider (LHC). A normalisi…
Scouting Super Resolution: from HLT-Level Reconstruction to Offline-like Quality
Maurizio Pierini
Data scouting, introduced by the CMS collaboration in 2011, is a technique to extend the reach of the experiment beyond what full-event storage bandwidth allows: rather than the co…
SigmaScale: LLM Compression with SVD-based Low-Rank Decomposition and Learned Scaling Matrices
Ernests Lavrinovics, Marco Letizia, Roy Janco +3
We present SigmaScale, a method for learning auxiliary scaling matrices to aid truncated Singular Value Decomposition (SVD) based Large Language Model (LLM) compression. Instea…
Taming the Exponential: A Fast Softmax Surrogate for Integer-Native Edge Inference
Dimitrios Danopoulos, Enrico Lupi, Michael Kagan +1
Softmax can become a computational bottleneck in the Transformer model's Multi-Head Attention (MHA) block, particularly in small models under low-precision inference, where exponen…
PQuantML: A Tool for End-to-End Hardware-aware Model Compression
Roope Niemi, Anastasiia Petrovych, Arghya Ranjan Das +9
PQuantML is a new open-source, hardware-aware neural network model compression library tailored to end-to-end workflows. Motivated by the need to deploy performant models to enviro…
CHLU: The Causal Hamiltonian Learning Unit as a Symplectic Primitive for Deep Learning
Pratik Jawahar, Maurizio Pierini
Current deep learning primitives dealing with temporal dynamics suffer from a fundamental dichotomy: they are either discrete and unstable (LSTMs) \citep{pascanu_difficulty_2013},…
Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs
Alberto Coppi, Ema Puljak, Lorenzo Borella +6
We present a systematic study of Tensor Network (TN) models $\unicode{x2013}$ Matrix Product States (MPS) and Tree Tensor Networks (TTN) $\unicode{x2013}$ for real-time jet tagging…
AIE4ML: An End-to-End Framework for Compiling Neural Networks for the Next Generation of AMD AI Engines
Dimitrios Danopoulos, Enrico Lupi, Chang Sun +4
Efficient AI inference on AMD's Versal AI Engine (AIE) is challenging due to tightly coupled VLIW execution, explicit datapaths, and local memory management. Prior work focused on…
Knowledge is Overrated: A zero-knowledge machine learning and cryptographic hashing-based framework for verifiable, low latency inference at the LHC
Pratik Jawahar, Caterina Doglioni, Maurizio Pierini
Low latency event-selection (trigger) algorithms are essential components of Large Hadron Collider (LHC) operation. Modern machine learning (ML) models have shown great offline per…
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
Ema Puljak, Maurizio Pierini, Artur Garcia-Saez
The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on t…
tn4ml: Tensor Network Training and Customization for Machine Learning
Ema Puljak, Sergio Sanchez-Ramirez, Sergi Masot-Llima +3
Tensor Networks have emerged as a prominent alternative to neural networks for addressing Machine Learning challenges in foundational sciences, paving the way for their application…
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
T. Aarrestad, M. van Beekveld, M. Bona +36
We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at…