SWAG合集

The rapid advancement of Artificial Intelligence (AI) has necessitated the development of specialized hardware architectures capable of efficient, real-time processing. Embedded AI hardware architectures, including neuromorphic processors and low-power AI accelerators, are at the forefront of this evolution. These technologies enable intelligent functionalities in edge devices, facilitating applications in autonomous vehicles, robotics, and Internet of Things (IoT) systems. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions.

This PhD project delves into the co-design of ultra-low-power AI hardware architectures tailored for edge computing applications. The research aims to develop neuromorphic processors, FPGA/ASIC-based AI accelerators, and intelligent interface designs that enable real-time, efficient processing in resource-constrained environments. Students will explore innovations in hardware-software integration, emphasizing energy efficiency, scalability, and adaptability to various applications such as autonomous systems, IoT devices, and wearable technologies.

Research Focus Areas:

1- Neuromorphic and AI-Optimized Processors: Design AI-specific chip architectures, including neuromorphic and domain-specific accelerators (e.g., TPUs, NPUs, FPGAs), for low-power and real-time AI processing.

2- Reconfigurable AI-Embedded Systems: Develop adaptive FPGA/ASIC architectures that dynamically reconfigure based on AI workloads, optimizing performance, energy efficiency, and functionality.

3- Intelligent Interface Design: Create smart interfaces that facilitate seamless integration between AI hardware components and embedded systems, ensuring efficient data flow and processing.

SWAG合集 offers a distinctive research environment renowned for its world-class programmes, cutting-edge facilities, and strong industry partnerships, attracting top-tier students and experts globally. As an internationally recognised leader in AI, embedded system design, and intelligent systems research, Cranfield fosters innovation through applied research, bridging academia and industry. Students will have access to state-of-the-art laboratories, hardware/software resources, and design facilities, supporting AI-powered electronics research.

This project will be conducted within Cranfield’s Integrated Vehicle Health Management (IVHM) Centre, established in 2008 in collaboration with industry leaders such as Boeing, Rolls-Royce, BAE Systems, Meggitt, and Thales. The IVHM Centre is globally recognized for defining the subject area and continues to expand its research horizons. It plays a pivotal role in the £65 million Digital Aviation Research and Technology Centre (DARTeC), leading advancements in aircraft electrification, autonomous systems, and secure intelligent hardware. Through collaborations with the Aerospace Integration Research Centre (AIRC), Airbus, and Rolls-Royce, students gain industry exposure and further research opportunities.

Additionally, the IVHM Centre hosts Seretonix, a research group specializing in secure electronic design, AI-driven system resilience, and intelligent hardware security. Through the EUROPRACTICE partnership, the IVHM Centre provides access to advanced CAD tools, integrated circuit prototyping, and technical training, equipping students with cutting-edge skills.

To support hands-on experimentation and applied research, the IVHM Centre offers access to a suite of specialised facilities:

• UAV Fuel Rig with Five Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics.
• Machine Fault Simulator for Rotating Machinery Faults: A versatile platform that replicates common faults in rotating machinery, such as imbalance and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms.
• Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining useful life of electronic components, supporting studies in electronic system reliability and maintenance strategies.
• Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter degradation.
• Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection, diagnostics, and prognostics in power generation systems.
• Auxiliary Power Unit (APU) Rig: Replicates the functions of an aircraft's APU, enabling research into fault detection, diagnostics, and health management of auxiliary power systems.
• Cranfield 737-400: Aircraft Instrumentation and Environmental Control Systems (AID, ECS): A full-scale Boeing 737-400 aircraft equipped with instrumentation for studying environmental control systems and other onboard systems, providing a realistic environment for research and training.
• SIU 737-200 ECS: A ground-based Boeing 737-200 Environmental Control System used for simulating faults and studying system behaviour under various conditions, aiding in the development of diagnostic and prognostic techniques.
• Hawk ECS: An Environmental Control System from a BAE Systems Hawk aircraft, utilized for research into thermal management and system health monitoring, supporting studies in military aircraft systems.

Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities and employability in the field of intelligent systems and AI-integrated electronics.

This research aims to revolutionize edge computing by developing ultra-low-power AI hardware architectures. By integrating neuromorphic processors and reconfigurable FPGA/ASIC-based accelerators, the project will enable real-time, intelligent processing in resource-constrained environments. Outcomes include the creation of adaptive hardware capable of on-device learning and decision-making, significantly reducing latency and energy consumption. These advancements will have broad applications in areas such as autonomous vehicles, wearable health monitors, and IoT devices, setting new standards for efficiency and performance in embedded AI systems. Given the global surge in demand for energy-efficient AI solutions, this research positions students at the forefront of technological innovation, equipping them with the expertise to lead in the development of next-generation intelligent hardware.

As embedded AI hardware continues to redefine intelligent computing, this PhD offers direct access to cutting-edge silicon design workflows through Cranfield’s EUROPRACTICE membership and partner labs. You’ll receive hands-on training in FPGA/ASIC prototyping, neuromorphic circuit design, and hardware–software integration. Opportunities include international collaboration with leading microelectronics research centres, funded travel to top-tier conferences like DATE, ISCAS, and NeurIPS, and active involvement in co-design with industry partners. The multi-phase research plan is publication-oriented, allowing you to build a strong academic and professional track record in energy-efficient and secure AI hardware innovation.

By the end of this PhD, candidates will possess advanced technical and design expertise in neuromorphic processors, low-power accelerators, and intelligent hardware interfaces. The project cultivates a strong foundation in system-level integration, digital design, and optimization of AI-integrated hardware architectures, all of which are highly valued in both academia and industry. Beyond technical skills, students will develop project leadership, scientific communication, and collaborative problem-solving capabilities, which are essential for success in innovation-driven environments. This training prepares graduates for impactful roles in semiconductor R&D, aerospace systems design, intelligent edge computing, and next-generation processor development.