Lakshan Ram Madhan Mohan, Alexander Marshall, Samuel Maddrell-Mander, Daniel O'Hanlon, Konstantinos Petridis, Jonas Rademacker, Victoria Rege, Alexander Titterton
This paper presents the first study of Graphcore's Intelligence Processing Unit (IPU) in the context of particle physics applications.
Comparisons are made for neural-network-based event simulation, multiple-scattering correction, and flavour tagging, implemented on IPUs, GPUs and CPUs, using a variety of neural network architectures and hyperparameters. Additionally, a Kálmán filter for track reconstruction is implemented with promising results.
Joseph Ortiz, Mark Pupilli, Stefan Leutenegger, Andrew J. Davison
This paper shows for the first time that the classical computer vision problem of bundle adjustment (BA) can be solved extremely fast on a graph processor such as Graphcore's Intelligence Processing Unit (IPU) using Gaussian Belief Propagation.
Gaussian Belief Propagation is an effective algorithmic framework for spatial AI problems where estimates are needed in real time with new measurements constantly being fed into the algorithm.
Ilyes Kacher, Maxime Portaz, Hicham Randrianarivo, Sylvain Peyronnet
Graphcore's architecture of the processor has been designed to achieve state of the art performance on current machine intelligence models for both training and inference.
In this paper, we report on a benchmark in which we have evaluated the performance of IPU processors on deep neural networks for inference. We focus on deep vision models such as ResNeXt. We report the observed latency, throughput and energy efficiency.
Zhe Jia, Blake Tillman, Marco Maggioni, Daniele Paolo Scarpazza
This report focuses on the architecture and performance of the Intelligence Processing Unit (IPU), a novel, massively parallel platform introduced by Graphcore and aimed at Artificial Intelligence/Machine Learning (AI/ML) workloads.
The study dissects the IPU’s performance behavior using microbenchmarks that were crafted for the purpose.
Dominic Masters, Carlo Luschi
The team at Graphcore Research addresses mini-batch stochastic gradient optimization of modern deep network architectures.
In this paper, we review common assumptions on learning rate scaling and training duration, as a basis for an experimental comparison of test performance for different mini-batch sizes. Our experiments show that small batch sizes produce the best results.
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