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Graphcore C2 Card performance for image-based deep learning application: A Report

2020-02-26 · Ilyes Kacher, Maxime Portaz, Hicham Randrianarivo, Sylvain Peyronnet

Recently, Graphcore has introduced an IPU Processor for accelerating machine learning applications. The 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.

📄 PDF Abstract BibTeX arXiv:2002.11670

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BIG-bench Machine Learning

Methods 이 논문이 사용한 방법론

1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…
ReLU How Do I Communicate to Expedia? How Do I Communicate to Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Live Support & Special Travel…
Batch Normalization 설명 없음
Kaiming Initialization 설명 없음
Residual Connection 설명 없음
Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…
Average Pooling 설명 없음
ResNeXt Block A ResNeXt Block is a type of residual block used as part of the ResNeXt CNN…

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