# Caffe con Troll: Shallow Ideas to Speed Up Deep Learning

arXiv: https://arxiv.org/abs/1504.04343 · HTML: https://mlsanity.com/paper/1504.04343/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2015.

## Built on by (most cited first, 4 of 4)

- [Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset](https://mlsanity.com/paper/2102.07655/) (2021)
- [Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs](https://mlsanity.com/paper/2209.13443/) (2022)
- [SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators](https://mlsanity.com/paper/2403.02946/) (2024)
- [High Performance Convolution Using Sparsity and Patterns for Inference in Deep Convolutional Neural Networks](https://mlsanity.com/paper/2104.08314/) (2021)

## Abstract (arXiv)

We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals. We built CcT to examine the performance characteristics of training and deploying general-purpose convolutional neural networks across different hardware architectures. We find that, by employing standard batching optimizations for CPU training, we achieve a 4.5x throughput improvement over Caffe on popular networks like CaffeNet. Moreover, with these improvements, the end-to-end training time for CNNs is directly proportional to the FLOPS delivered by the CPU, which enables us to efficiently train hybrid CPU-GPU systems for CNNs.