Domain-Specific Computer Architectures

This chapter brings together the topics discussed in previous chapters as we examine a variety of computer system architectures designed to meet unique user requirements. We will gain an understanding of the user-level requirements and performance capabilities associated with several categories of real-world computer systems.

This chapter will cover the following topics:

• Architecting computer systems to meet unique requirements
• Smartphone architecture
• Personal computer architecture
• Warehouse-scale computing architecture
• Neural networks and machine learning architectures

After completing this chapter, you will understand the decision process used in defining computer architectures to support specific needs. You will have learned the key requirements driving the architectural designs of mobile devices, personal computers, cloud server systems, and neural networks and other machine learning architectures...

Technical requirements

The files for this chapter, including answers to the exercises, are available at https://github.com/PacktPublishing/Modern-Computer-Architecture-and-Organization-Second-Edition.

Architecting computer systems to meet unique requirements

Every device containing a digital processor is designed to perform a particular function or collection of functions. This includes general-purpose devices such as personal computers. A comprehensive list of the required and desired features and capabilities for a particular device or computer system provides the raw information to begin designing the architecture of its digital components.

The list that follows identifies some of the considerations a computer architect must weigh in the process of organizing the design of a digital system:

• The types of processing required: Does the device need to process audio, video, or other analog information? Is a high-resolution graphics display included in the design? Will extensive floating-point or integer mathematics be required? Will the system support multiple, simultaneously running applications? Are special algorithms, such as neural network processing, going to...

Smartphone architecture

At the architectural level, there are three key features a smartphone must provide to gain wide acceptance: small size (except for the display), long battery life, and very high processing performance upon demand. Obviously, the requirements for long battery life and high processing power are in conflict and must be balanced to achieve an optimal design.

The requirement for small size is generally approached by starting with a screen size (in terms of height and width) large enough to render high-quality video and function as a user-input device (especially as a keyboard), yet small enough to be easily carried in a pocket or purse. To keep the overall device size small in terms of total volume, it must be as thin as possible.

In the quest for thinness, the mechanical design must provide sufficient structural strength to support the screen and resist damage from routine handling, drops on the floor, and other physical assaults, while simultaneously providing...

Personal computer architecture

The next system we’ll examine is a gaming PC with a processor that, at the time of writing (in late 2021), leads the pack in terms of raw performance. We will look in detail at the system processor, the GPU, and the computer’s major subsystems.

Alienware Aurora Ryzen Edition R10 gaming desktop

The Alienware Aurora Ryzen Edition R10 desktop PC is designed to provide maximum performance for gaming applications. To achieve peak speed, the system architecture is built around the fastest main processor, GPU, memory, and disk subsystems available at prices that at least some serious gamers and other performance-focused users are willing to tolerate. However, the number of customers for the high-end configuration options described in this section is likely to be limited by its cost, which is over US $4,000.

The Aurora Ryzen Edition R10 is available with a variety of AMD Ryzen processors at varying performance levels and price points...

Warehouse-scale computing architecture

Providers of large-scale computing capabilities and networking services to the public and to sprawling organizations such as governments, research universities, and major corporations often aggregate computing capabilities in large buildings, each containing perhaps thousands of computers.

To make the most effective use of these capabilities, it is not sufficient to consider the collection of computers in a warehouse-scale computer (WSC) as simply a large number of individual computers. Instead, in consideration of the immense quantity of processing, networking, and storage capability provided by a warehouse-scale computing environment, it is more appropriate to think of the entire data center as a single, massively parallel computing system.

Early electronic computers were huge systems, occupying large rooms. Since then, computer architectures have evolved to today’s fingernail-size processor chips possessing vastly more computing...

Neural networks and machine learning architectures

We briefly studied the architecture of neural networks in Chapter 6, Specialized Computing Domains. This section examines the inner workings of a high-performance, dedicated neural net processor.

Intel Nervana neural network processor

In 2019, Intel announced the release of a pair of new processors, one optimized for the task of training sophisticated neural networks and the other for using trained networks to conduct inference, which is the process of generating neural network outputs given a set of input data.

The Nervana neural network processor for training (NNP-T) is essentially a miniature supercomputer tailored to the computational tasks required in the neural network training process. The NNP-T1000 is available in the following two configurations:

• The NNP-T1300 is a dual-slot PCIe card suitable for installation in a standard PC. It communicates with the host via PCIe 4.0 x16. It is possible to connect...

Summary

This chapter presented several computer system architectures tailored to particular user needs and identified some key features associated with each of them. We looked at application categories including smartphones, gaming-focused personal computers, warehouse-scale computing, and neural networks. These examples provided a connection between the more theoretical discussions of computer and systems architectures and components presented in earlier chapters and the real-world implementations of modern, high-performance computing systems.

Having completed this chapter, you should understand the decision processes used in defining computer architectures to support specific user needs. You have gained insight into the key requirements driving smart mobile device architectures, high-performance personal computing architectures, warehouse-scale cloud-computing architectures, and advanced machine learning architectures.

The next chapter presents the categories of cybersecurity...

Exercises

1. Draw a block diagram of the computing architecture for a system to measure and report weather data 24 hours a day at 5-minute intervals using SMS text messages. The system is battery powered and relies on solar cells to recharge the battery during daylight hours. Assume the weather instrumentation consumes minimal average power, only requiring full power momentarily during each measurement cycle.
2. For the system of Exercise 1, identify a suitable commercially available processor and list the reasons that processor is a good choice for this application. Factors to consider include cost, processing speed, tolerance of harsh environments, power consumption, and integrated features such as RAM and communication interfaces.

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