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.
