10 citations · 20 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation
Benjamin Hawks, Jason Weitz, Dmitri Demler +13
As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantl…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml
Zhixing Jiang, Dennis Yin, Yihui Chen +7
This study presents an efficient implementation of transformer architectures in Field-Programmable Gate Arrays(FPGAs) using hls4ml. We demonstrate the strategy for implementing the…
Reliable edge machine learning hardware for scientific applications
Tommaso Baldi, Javier Campos, Ben Hawks +15
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementatio…
Ultra Fast Transformers on FPGAs for Particle Physics Experiments
Zhixing Jiang, Dennis Yin, Elham E Khoda +6
This work introduces a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) by using the \texttt{hls4ml} tool. Given the demons…
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
Ho Fung Tsoi, Vladimir Loncar, Sridhara Dasu +1
Compact symbolic expressions have been shown to be more efficient than neural network models in terms of resource consumption and inference speed when implemented on custom hardwar…