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Deep Neural Networks for Image-Based Dietary Assessment
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Caffe con Troll: Shallow Ideas to Speed Up Deep Learning.

Stefan Hadjis1, Firas Abuzaid1, Ce Zhang2

  • 1Stanford University.

Proceedings of the Fourth Workshop on Data Analytics at Scale (Danac 2015) : May 31St, 2015, Melbourne, Australia. Workshop on Data Analytics in the Cloud (4Th : 2015 : Melbourne, Vic.)
|June 18, 2016
PubMed
Summary

We developed Caffe con Troll (CcT), a new version of Caffe, improving CPU training throughput by 4.5x. This allows efficient training of convolutional neural networks (CNNs) on hybrid CPU-GPU systems.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • The Caffe framework is widely used for deep learning.
  • Evaluating the performance of deep learning models across diverse hardware is crucial.
  • Optimizing training and deployment of convolutional neural networks (CNNs) requires efficient software frameworks.

Purpose of the Study:

  • To introduce Caffe con Troll (CcT), a fully compatible, internally rebuilt version of Caffe.
  • To analyze the performance of CNN training and deployment on various hardware architectures.
  • To investigate performance improvements using CPU-centric optimizations.

Main Methods:

  • Rebuilt the internals of the Caffe framework to create Caffe con Troll (CcT).
  • Implemented standard batching optimizations for CPU training.
  • Tested performance on popular CNNs, such as CaffeNet, across different hardware.

Main Results:

  • Achieved a 4.5× throughput improvement for CPU training compared to the original Caffe.
  • Demonstrated that end-to-end CNN training time is directly proportional to CPU FLOPS.
  • Enabled efficient training of hybrid CPU-GPU systems for CNNs.

Conclusions:

  • Caffe con Troll (CcT) offers significant performance enhancements for CPU-based deep learning.
  • The findings provide a clear relationship between computational power and training time for CNNs.
  • CcT facilitates more efficient utilization of hybrid computing resources for deep learning tasks.