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Deep Learning Performance: Scale-up vs Scale-out

Architectures & Technologies Dell EMC | Infrastructure Solutions Group

2 Introduction

Figure 1: Artificial Intelligence, Machine Learning and Deep Learning [Source: MIT]

Artificial Intelligence

First coined in 1956 by John McCarthy, AI involves machines that can perform tasks that are

characteristic of human intelligence. While this is rather general, it includes things like planning,

understanding language, recognizing objects and sounds, learning, and problem solving.

Machine Learning

Arthur Samuel coined the phrase not too long after AI, in 1959, defining it as, “the ability to learn

without being explicitly programmed.” You see, you can get AI without using machine learning,

but this would require building millions of lines of codes with complex rules and decision-trees.

So instead of hard coding software routines with specific instructions to accomplish a task,

machine learning is a way of “training” an algorithm so that it can learn how. “Training” involves

feeding massive amounts of data to the algorithm and allowing the algorithm to adjust itself and

improve.

Spiking Neural Networks

Spiking neural networks (SNNs) are artificial neural network models that more closely mimic

natural neural networks. In addition to neuronal and synaptic state, SNNs also incorporate the

concept of time into their operating model. The idea is that neurons in the SNN do not fire at

each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather

fire only when a membrane potential – an intrinsic quality of the neuron related to its membrane

electrical charge – reaches a specific value. When a neuron fires, it generates a signal which