
Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations
The precise simulation of particle transport through detectors remains a...
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Learning Particle Physics by Example: LocationAware Generative Adversarial Networks for Physics Synthesis
We provide a bridge between generative modeling in the Machine Learning ...
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WRPN: Training and Inference using Wide ReducedPrecision Networks
For computer vision applications, prior works have shown the efficacy of...
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Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks
High energy physics experiments rely heavily on the detailed detector si...
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Accelerating GAN training using highly parallel hardware on public cloud
With the increasing number of Machine and Deep Learning applications in ...
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WRPN: Wide ReducedPrecision Networks
For computer vision applications, prior works have shown the efficacy of...
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Generative Models for Fast Calorimeter Simulation.LHCb case
Simulation is one of the key components in high energy physics. Historic...
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Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case
Deep learning is finding its way into high energy physics by replacing traditional Monte Carlo simulations. However, deep learning still requires an excessive amount of computational resources. A promising approach to make deep learning more efficient is to quantize the parameters of the neural networks to reduced precision. Reduced precision computing is extensively used in modern deep learning and results to lower execution inference time, smaller memory footprint and less memory bandwidth. In this paper we analyse the effects of low precision inference on a complex deep generative adversarial network model. The use case which we are addressing is calorimeter detector simulations of subatomic particle interactions in accelerator based high energy physics. We employ the novel Intel low precision optimization tool (iLoT) for quantization and compare the results to the quantized model from TensorFlow Lite. In the performance benchmark we gain a speedup of 1.73x on Intel hardware for the quantized iLoT model compared to the initial, not quantized, model. With different physicsinspired selfdeveloped metrics, we validate that the quantized iLoT model shows a lower loss of physical accuracy in comparison to the TensorFlow Lite model.
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