Advancements in Microprocessor Architecture for Ubiquitous AI-An Overview on History, Evolution, and Upcoming Challenges in AI Implementation

Abstract

Artificial intelligence (AI) has successfully made its way into contemporary industrial sectors such as automobiles, defense, industrial automation 4.0, healthcare technologies, agriculture, and many other domains because of its ability to act autonomously without continuous human interventions. However, this capability requires processing huge amounts of learning data to extract useful information in real time. The buzz around AI is not new, as this term has been widely known for the past half century. In the 1960s, scientists began to think about machines acting more like humans, which resulted in the development of the first natural language processing computers. It laid the foundation of AI, but there were only a handful of applications until the 1990s due to limitations in processing speed, memory, and computational power available. Since the 1990s, advancements in computer architecture and memory organization have enabled microprocessors to deliver much higher performance. Simultaneously, improvements in the understanding and mathematical representation of AI gave birth to its subset, referred to as machine learning (ML). ML includes different algorithms for independent learning, and the most promising ones are based on brain-inspired techniques classified as artificial neural networks (ANNs). ANNs have subsequently evolved to have deeper and larger structures and are often characterized as deep neural networks (DNN) and convolution neural networks (CNN). In tandem with the emergence of multicore processors, ML techniques started to be embedded in a range of scenarios and applications. Recently, application-specific instruction-set architecture for AI applications has also been supported in different microprocessors. Thus, continuous improvement in microprocessor capabilities has reached a stage where it is now possible to implement complex real-time intelligent applications like computer vision, object identification, speech recognition, data security, spectrum sensing, etc. This paper presents an overview on the evolution of AI and how the increasing capabilities of microprocessors have fueled the adoption of AI in a plethora of application domains. The paper also discusses the upcoming trends in microprocessor architectures and how they will further propel the assimilation of AI in our daily lives.

Keywords: application-specific integrated circuits; artificial intelligence; automation; instruction set architecture; intelligent systems; machine learning; microprocessors; multicores; real-time processing.

Figure 1

Taxonomy of AI and its sub-fields.

Figure 2

Generic operation of an Expert System.

Figure 3

Artificial intelligence over the years.

Figure 4

Five stages of pipelining in microprocessors.

Figure 5

Trend of heat dissipation with the increase in power density of Intel chips.

Figure 6

Performance evolution of general-purpose microprocessors.

Figure 7

Comparison between CPU operations per second for single core and dual core.

Figure 8

Single-instruction multiple-data (SIMD) operation.

Figure 9

Mapping of fully connected (FC) layers onto matrix multiplication.

Figure 10

Mapping of convolution (Conv) layers onto matrix multiplication.

Figure 11

Hardware block diagram showing the generic structure of a spatial architecture.