  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.
