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This paper presents cltorch, a hardware-agnostic backend for the Torch neuralnetwork framework. cltorch enables training of deep neural networks on GPUsfrom diverse hardware vendors, including AMD, NVIDIA, and Intel. cltorchcontains sufficient implementation to run models such as AlexNet, VGG,Overfeat, and GoogleNet. It is written using the OpenCL language, a portablecompute language, governed by the Khronos Group. cltorch is the top-rankedhardware-agnostic machine learning framework on Chintala's convnet-benchmarkspage.  This paper presents the technical challenges encountered whilst creating thecltorch backend for Torch, and looks in detail at the challenges related toobtaining a fast hardware-agnostic implementation.  The convolutional layers are identified as the key area of focus foraccelerating hardware-agnostic frameworks. Possible approaches to acceleratingthe convolutional implementation are identified including: implementation ofthe convolutions using the implicitgemm or winograd algorithm, using a GEMMimplementation adapted to the geometries associated with the convolutionalalgorithm, or using a pluggable hardware-specific convolutional implementation.

cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL