PhD in Computer Architecture and High-Performance Digital Circuit Design for Edge AI Computing
Explore novel designs in digital and computer architecture to boost edge AI computing. Develop hardware-aware machine learning techniques to optimize circuits for performance and efficiency in embedded systems.
AI-generated overview
Project Description
Project Overview
The PhD project focuses on the design and optimization of high-performance digital architectures and circuits tailored for edge computing applications in machine learning and telecommunications. Research includes developing efficient hardware-aware optimization techniques such as quantization and pruning to improve the performance and energy efficiency of deep learning systems.
What You Will Do
You will design digital circuits and systems using hardware description languages and FPGA/ASIC tools, optimize machine learning models for hardware implementation, and collaborate with interdisciplinary teams to develop novel edge computing solutions.
Expected Outcomes
The research aims to produce innovative high-efficiency circuit designs and optimization methods for real-time machine learning inference on embedded devices, potentially enhancing performance and reducing power consumption in practical applications.
Why This Matters
As AI moves toward decentralized and resource-constrained environments, this work supports the development of efficient edge devices. Improved architectures can enable faster and more reliable AI in telecommunications, autonomous systems, and embedded computing.
Entry Requirements
How to Apply
Eligibility
Supervisor Profile
Dr. Shervin Vakili is an Assistant Professor at INRS-EMT specializing in computer architecture and high-performance architectures for real-time embedded systems. His research covers digital circuit design, hardware-aware machine learning, and energy-efficient architectures. He has multiple publications on optimization methods and accelerator designs in IEEE Transactions and top conferences.