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Found 24 papers, 12 papers with code
Building Machine Learning Challenges for Anomaly Detection in Science
We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR).
SymbolFit: Automatic Parametric Modeling with Symbolic Regression
In this work, we develop a framework that automates and streamlines the process by utilizing symbolic regression, a machine learning technique that explores a vast space of candidate functions without requiring a predefined functional form because the functional form itself is treated as a trainable parameter, making the process far more efficient and effortless than traditional regression methods.
Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml
This study presents an efficient implementation of transformer architectures in Field-Programmable Gate Arrays(FPGAs) using hls4ml.
Reliable edge machine learning hardware for scientific applications
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing.
Gradient-based Automatic Mixed Precision Quantization for Neural Networks On-Chip
Model size and inference speed at deployment time, are major challenges in many deep learning applications.
Ultrafast jet classification on FPGAs for the HL-LHC
Three machine learning models are used to perform jet origin classification.
Ultra Fast Transformers on FPGAs for Particle Physics Experiments
This work introduces a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) by using the \texttt{hls4ml} tool.
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
In this work, we propose $\tt{SymbolNet}$, a neural network approach to symbolic regression specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs.
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks
Neural networks achieve state-of-the-art performance in image classification, speech recognition, scientific analysis and many more application areas.
Symbolic Regression on FPGAs for Fast Machine Learning Inference
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints.
Tailor: Altering Skip Connections for Resource-Efficient Inference
We argue that while a network's skip connections are needed for the network to learn, they can later be removed or shortened to provide a more hardware efficient implementation with minimal to no accuracy loss.
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted.
QONNX: Representing Arbitrary-Precision Quantized Neural Networks
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks.
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving.
Lightweight Jet Reconstruction and Identification as an Object Detection Task
We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point.
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
The proposed approach has been evaluated based on two LSTM models, targeting a ZYNQ 7045 FPGA and a U250 FPGA.
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
We demonstrate that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile.
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.
Fast convolutional neural networks on FPGAs with hls4ml
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs.
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks.
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering.
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore reduction in model size, latency and energy consumption.
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
We discuss the trade-off between model accuracy and resource consumption.
Fast inference of Boosted Decision Trees in FPGAs for particle physics
We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process.
