The Core Technologies of Deep Learning

When the movie The Terminator was released in 1984, the notion of computers becoming self-aware seemed so futuristic that it was almost difficult to fathom. But just 22 years later, computers are rapidly gaining the ability to autonomously learn, predict, and adapt through the analysis of massive datasets. And luckily for us, the result is not a nuclear holocaust as the movie predicted, but new levels of data-driven innovation and opportunities for competitive advantage for a variety of enterprises and industries.

Artificial intelligence (AI) continues to play an expanding role in the future of high-performance computing (HPC). As machines increasingly become able to learn and even reason in ways similar to humans, we’re getting closer to solving the tremendously complex social problems that have always been beyond the realm of compute. Deep learning, a branch of machine learning, uses multi-layer artificial neural networks and data-intensive training techniques to refine algorithms as they are exposed to more data. This process emulates the decision-making abilities of the human brain, which until recently was the only network that could learn and adapt based on prior experiences.

Deep learning networks have grown so sophisticated they’ve begun to deliver even better performance than traditional machine learning approaches. One advantage of deep learning is that there is little need to "train" the system and define features that might be useful for modeling and prediction. With only basic labeling, machines can now learn these features independently as more data is introduced to the model. Deep learning has even begun to surpass the capabilities and speed of the human brain in many areas, including image, speech, or text classification, natural language processing, and pattern recognition.

The core technologies required for deep learning are very similar to those necessary for data-intensive computing and HPC applications. Here are a few technologies that are well-positioned to support deep learning networks.

Multi-core processors:

Deep learning applications require substantial amounts of processing power, and a critical element to the success and usability of deep learning comes with the ability to reduce execution times. Multi-core processor architectures currently dominate the TOP500 list of the most powerful supercomputers available today, with 91% based on Intel processors. Multiple cores can run numerous instructions at the same time, increasing the overall processing speed for compute-intensive programs like deep learning, while reducing power requirements, increasing performance, and allowing for fault tolerance.

The Intel® Xeon Phi™ Processor, which features a whopping 72 cores, is geared specifically for high-level HPC and deep learning. These many-core processors can help data scientists significantly reduce training times and run a wider variety of workloads, something that is critical to the computing requirements of deep neural networks.

Software frameworks and toolkits:

There are various frameworks, libraries, and tools available today to help software developers train and deploy deep learning networks, such as Caffe, Theano, Torch, and the HPE Cognitive Computing Toolkit. Many of these tools are built as resources for those new to deep learning systems, and aim to make deep neural networks available to those that might be outside of the machine learning community. These tools can help data scientists significantly reduce model training times and accelerate time to value for their new deep learning applications.

Deep learning hardware platforms:

Not every server can efficiently handle the compute-intensive nature of deep learning environments. Hardware platforms that are purpose-built to handle these requirements will offer the highest levels of performance and efficiency. New HPE Apollo systems contain a high ratio of GPUs to CPUs in a dense 4U form factor, which enables scientists to run deep learning algorithms faster and more efficiently while controlling costs.

Enabling technologies for deep learning is ushering in a new era of cognitive computing that promises to help us solve the world’s greatest challenges with more efficiency and speed than ever before. As these technologies become faster, more available, and easier to implement, deep learning technologies will secure their place in real-world applications – not in science fiction.