Explainable AI Guided Hardware Acceleration for Energy-Efficient AI Systems
Explore designing energy-efficient hardware to boost explainable AI models like large language models. Engage in cutting-edge research integrating AI, hardware design, and sustainability. Join a collaborative lab with strong mentorship at University of Maine.
AI-generated overview
Project Description
Project Overview
This PhD research is situated in the GENIUS Laboratory at the University of Maine, targeting Explainable AI (XAI) Guided Hardware Acceleration. The goal is to develop novel energy-efficient hardware solutions that facilitate transparent and interpretable AI by leveraging approximate computing techniques. This cutting-edge project supports AI models including large language models (LLMs) and reinforcement learning.
What You Will Do
The successful candidate will work closely with Prof. Ayesha Siddique and the GENIUS Lab team to design and prototype hardware accelerators that improve the computational efficiency of explainable AI. Research will include advanced programming, AI model analysis, and hardware-software co-design, focusing on sustainability and reliability.
Expected Outcomes
You will contribute to advancing academic knowledge in explainable AI while producing tangible hardware designs that promote greener AI technologies. Deliverables include publications, tools for AI interpretability, and practical hardware demonstrations suited for real-world sensitive applications.
Why This Matters
Explainable AI is critical for trustworthy deployment of AI in safety-sensitive domains. Hardware acceleration addresses computational challenges by reducing energy usage and latency, thus enabling scalable, reliable AI. This research furthers sustainable AI innovation with potential impacts in healthcare, finance, and autonomous systems.
Entry Requirements
How to Apply
Eligibility
Supervisor Profile
Prof. Ayesha Siddique leads research on Explainable AI Guided Hardware Acceleration, focusing on developing sustainable and interpretable AI systems using approximate computing. Her work bridges artificial intelligence, hardware design, and efficient computing technologies. She is an emerging expert in AI transparency and hardware-based acceleration techniques.