White Papers

ManualsBrandsDell ManualsConverged InfrastructureServers Solution Resources

PowerEdge Product Group

Direct from

Development

Building the Optimal Machine Learning Platform

Tech Note by:

Austin Shelnutt

Paul Steeves

SUMMARY

Machine Learning customers

have more choices than ever for

neural network models and

frameworks.

Those choices impact the type,

number, and form factor of the

preferred accelerator, the

dataflow topology between

accelerators and CPUs, the

amount and speed of direct

attached storage, and the

necessary bandwidth of I/O

devices.

This tech note provides a brief

overview of some of the basic

principles of Machine Learning

and describes the challenges

and trade-offs involved in

constructing the optimal

Machine Learning platform for

different use cases.

While various forms of machine learning have existed for several decades, the

past few years of development have yielded some extraordinary progress in

democratizing the capabilities and use cases for artificial intelligence in a wide

multitude of industries. Image classification, voice recognition, fraud detection,

medical diagnostics, and process automation are just a handful of the

burgeoning use cases for machine learning that are reinventing the very world

we live in. This blog provides a brief overview of some of the basic principles of

Machine Learning and describes the challenges and trade-offs involved in

constructing the optimal Machine Learning platform for different use cases.

Neural Networks are key to machine learning

At the center of the growth in machine learning is a modeling technique referred

to as neural networks (also known as deep neural networks, or deep learning),

which is based on our understanding of how the human brain learns and

processes information. Neural networks are not a new concept, and have been

proposed as a model for computational learning since the 1940’s. What makes

neural networks so attractive for machine learning is that they provide a

mathematical ecosystem that allows the decision making accuracy of a

computer to scale beyond explicit programming rules and, in a sense, learn

from experience.

Previously, the limiting factor of neural network models has been that they are

extremely computation intensive and require a tremendous amount of labeled

data input to be able to “learn”. This double hurdle of processing power and

available data had prevented them from becoming relevant…. until now.

Summary of content (5 pages)

PAGE 1
Direct from Development PowerEdge Product Group Building the Optimal Machine Learning Platform Tech Note by: Austin Shelnutt Paul Steeves SUMMARY While various forms of machine learning have existed for several decades, the past few years of development have yielded some extraordinary progress in democratizing the capabilities and use cases for artificial intelligence in a wide multitude of industries.
PAGE 2
The Machine Learning platform stack As shown in in the figure to the right, the machine learning stack consists of:  The neural network (application) layer – this is the data analysis model  The framework layer - provides the specialized software neural networks run on  The math libraries layer - houses the math routines the frameworks call  The operating system layer – choice of OS  The hardware platform layer -offers a number of different accelerator options The platform choices made at eac
PAGE 3
When designing the optimal platform to use for a neural network, how that particular neural network is constructed is crucial in determining what options are best for it at other layers of the stack. In general, the platform designer’s goal is to understand how data is moved in, out, and around inside of the system to tune features in a manner that most efficiently eliminates data choke points or bottlenecks.
PAGE 4
Hardware Platform Layer Choosing the right hardware technology to support a given machine learning application is another challenge for platform design. While CPUs can be used for deep learning, they are scalar multiplication engines by nature, and poorly suited to the higher-order tensor operations common to deep learning (vectors, matrices, and beyond). So machine learning platforms typically incorporate some form of accelerator technology - GPU, FPGA, or ASIC.
PAGE 5
Direct from Development PowerEdge Product Group Specialized accelerator-to-accelerator communication Many technology companies are now implementing specialized accelerator-to-accelerator connection links, as conceptualized in Figure 6 below. Nvidia’s NVlink is an example of a specialized communication path that dramatically improves bandwidth between accelerators for applications that benefit from peer-to-peer data exchange.