SALT LAKE CITY, Nov. 14, 2016 /PRNewswire/ -- IBM (NYSE: IBM) and NVIDIA (NASDAQ: NVDA) today announced collaboration on a new deep learning tool optimized for the latest IBM and NVIDIA technologies to help train computers to think and learn in more human-like ways at a faster pace.
Deep learning is a fast growing machine learning method that extracts information by crunching through millions of pieces of data to detect and rank the most important aspects from the data. Publicly supported among leading consumer web and mobile application companies, deep learning is quickly being adopted by more traditional business enterprises. Deep learning and other artificial intelligence capabilities are being used across a wide range of industry sectors; in banking to advance fraud detection through facial recognition; in automotive for self-driving automobiles and in retail for fully automated call centers with computers that can better understand speech and answer questions. A new deep learning software toolkit available today called IBM PowerAI runs on the recently announced IBM server built for artificial intelligence that features NVIDIA® NVLink™ interconnect technology optimized for IBM's Power architecture. The hardware-software solution provides more than 2X performance over comparable servers with 4 GPUs running AlexNet with Caffe. 1 The same 4-GPU Power-based configuration running Alexnet with BVLC Caffe can also outperform 8 M40 GPU-based x86 configurations 2, making it the world's fastest commercially available enterprise systems platform on two versions of a key deep learning framework. Caffe is a widely-used deep learning framework developed by Berkeley Vision and Learning Center (BVLC) and is recognized within the technology industry as one of the most popular deep learning community applications. Caffe is one of five deep learning software frameworks available in the IBM PowerAI toolkit. The toolkit leverages NVIDIA GPUDL libraries including cuDNN, cuBLAS and NCCL as part of NVIDIA SDKs to deliver multi-GPU acceleration on IBM servers.