Softmax Optimizations for Intel Xeon Processor-based Platforms

04/28/2019
by   Jacek Czaja, et al.
0

Softmax is popular normalization method used in machine learning. Deep learning solutions like Transformer or BERT use the softmax function intensively, so it is worthwhile to optimize its performance. This article presents our methodology of optimization and its results applied to softmax. By presenting this methodology, we hope to increase an interest in deep learning optimizations for CPUs. We believe that the optimization process presented here could be transferred to other deep learning frameworks such as TensorFlow or PyTorch.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/23/2020

Applying the Roofline model for Deep Learning performance optimizations

In this paper We present a methodology for creating Roofline models auto...
research
05/08/2018

Online normalizer calculation for softmax

The Softmax function is ubiquitous in machine learning, multiple previou...
research
04/10/2020

Efficient Sampled Softmax for Tensorflow

This short paper discusses an efficient implementation of sampled softma...
research
01/13/2020

The Two-Pass Softmax Algorithm

The softmax (also called softargmax) function is widely used in machine ...
research
08/04/2022

Leveraging the HW/SW Optimizations and Ecosystems that Drive the AI Revolution

This paper presents a state-of-the-art overview on how to architect, des...
research
06/20/2022

Revisiting lp-constrained Softmax Loss: A Comprehensive Study

Normalization is a vital process for any machine learning task as it con...
research
03/09/2019

SSN: Learning Sparse Switchable Normalization via SparsestMax

Normalization methods improve both optimization and generalization of Co...

Please sign up or login with your details

Forgot password? Click here to reset