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Found 12 papers, 5 papers with code
FPGA Deployment of LFADS for Real-time Neuroscience Experiments
Our implementation shows an inference latency of 41. 97 $\mu$s for processing the data in a single trial on a Xilinx U55C.
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.
Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs
In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits.
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.
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark
We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms.
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.
Physics Community Needs, Tools, and Resources for Machine Learning
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges.
Graph Neural Networks for Charged Particle Tracking on FPGAs
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC).
Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
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.
FPGAs-as-a-Service Toolkit (FaaST)
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years.
Computational Physics Distributed, Parallel, and Cluster Computing High Energy Physics - Experiment Data Analysis, Statistics and Probability Instrumentation and Detectors
FPGA-accelerated machine learning inference as a service for particle physics computing
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains.
Data Analysis, Statistics and Probability High Energy Physics - Experiment Computational Physics Instrumentation and Detectors
