This module introduces Year 1 students to the exciting world where Artificial Intelligence meets computer microchips. The primary aim is to show students how AI software actually runs on different types of hardware, from everyday computer processors (CPUs) to the specialized chips found in smartphones and smart devices (NPUs and embedded systems). Students will explore how these chips have changed over time and where the technology is heading next. As a hands-on engineering class, students will learn to look at different chip designs and understand the basic balance between speed, size, and battery power. By applying simple digital circuit concepts, students will learn how to design and improve basic hardware specifically built to run AI programs. Ultimately, this course aims to change how students look at technology—moving them from simply using AI apps to understanding how to build the physical chips that power them. The project challenges are designed to help students build the independent thinking and problem-solving skills they need for a future career in tech and engineering.
A Describe the fundamental architectural components of AI accelerator design, identifying key structural elements and their interrelationships within hardware systems B Explain the implementation of AI algorithms across CPU, GPU, and NPU hardware platforms, evaluating the relative strengths, limitations, and suitability of each architecture for specific computational tasks C Investigate the application of AI accelerators within the Internet of Things (IoT) sector, drawing on current industry literature and case studies to assess their impact on edge computing performance and energy efficiency D Design a simple logic circuit diagram for a basic AI-related function, applying appropriate design principles and justifying component selection with reference to technical criteria and responsible digital practice.
This module adopts a blended pedagogical approach designed to build foundational academic competencies and provide structured readiness for the Academic Pathways Readiness Block in Semester 2 Year 1. 1. Lectures adopt interactive discussions on current AI applications, industry developments, and emerging technologies. 2. Case studies and demonstrations are also provided to illustrate how AI algorithms are implemented on modern hardware platforms. 3. Hands-on guided exercises using simple AI software tools and hardware demonstrations (where available). 4. Students demonstrate on selected AI applications or emerging AI hardware technologies to encourage independent learning and communication skills. 5. Feedback use is structured progressively: students receive formative feedback at key milestones and are explicitly guided in reflection and assessment activities, enabling students to evaluate their own development, identify areas for growth, and articulate their readiness for increased academic challenge.