Intel Xeon Phi Processor Code Modernization Nets Over 55x Faster NeuralTalk2 Image Tagging - insideBIGDATA

In this special guest feature, Rob Farber from TechEnablement writes that modernized code can deliver significant speedups on machine learning applications. Benchmarks, customer experiences, and the technical literature have shown that code modernization can greatly increase application performance on both Intel Xeon and Intel Xeon Phi processors. Colfax Research recently published a study showing that image tagging performance using the open source NeuralTalk2 software can be improved 28x on Intel Xeon processors and by over 55x on the latest Intel Xeon Phi processors (specifically an Intel Xeon Phi processor 7210). For the study, Colfax Research focused on modernizing the C-language Torch middleware while only one line was changed in the high-level Lua scripts. NeuralTalk2 uses machine learning algorithms to analyze real-life photographs of complex scenes and produce a correct textual description of the objects in the scene and relationships between them (e.g., "a cat is sitting on a couch", "woman is holding a cell phone in her hand", "a horse-drawn carriage is moving through a field", etc.) Captioned examples are show in the figure below.