Accelerators for Combinatorial Optimization Problem
This project aims to explore and implement accelerators on FPGAs or ASICs for Combinatorial Optimization Problems, such as Boolean Satisfiability (SAT) problems. The design flow includes writing RTL Verilog code, developing FPGA commands, compiling and programming the FPGA board. Innovative solutions may have the opportunity to be implemented in GlobalFoundries 12nm FinFET technology and tested in the lab.
Required Knowledge & Skills:
- Digital circuits
- FPGA development
- ModelSim, Quartus
- Verilog, SystemVerilog
Supervisor: Prof. Shiyu Su
Email: shiyu.su@uwaterloo.ca
Application of neural network or deep learning algorithms in pattern recognition
This project aims to build deep machine learning models (e.g. recurrent neural networks, transformer models, etc.) to perform experimental data analysis where tasks of classification and pattern recognition are performed.
Required knowledge & skills: Python, machine learning algorithms.
Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Location: Remote/RAC-2101
Application of quantum and classical machine learning algorithms in real-life data analysis
This project aims to build quantum and classical deep machine learning models (e.g., recurrent neural networks, transformer models, etc.) to assess real-life big data, where tasks such as classification, anomaly detection, and pattern recognition are performed.
Required skills: Python, machine learning algorithms.
Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Phone: 519-888-4567 x30481
Location: Remote/RAC-2101
Comparing the accuracy of bone density measurements obtained from a dual energy chest radiograph to a state-of-the-art DEXA scan
DEXA is the established gold standard when it comes to bone density measurements. However, it is a test that requires specialized equipment and is prescribed for a very targeted population e.g. seniors. In contrast, X-ray imaging is a commonly prescribed imaging test in outpatient clinics and emergency departments across Canada. This project will investigate the difference in error between a bone density measurement extrapolated from a dual energy X-ray image and compare it to the measurement obtained from a Dual Energy X-ray Absorptiometry (DEXA) scan.
Supervisor: Prof. Karim S. Karim
Email: kkarim@uwaterloo.ca
Data Denoising and Preprocessing for Scientific Applications
This project aims to apply and adapt existing denoising algorithms to scientific datasets, such as medical or chemistry data. The goal is to improve data quality and prepare it for subsequent analysis in larger research pipelines. Students will start by using existing Python code for denoising (e.g., for 1D signals or 2D images) and are encouraged to explore alternative preprocessing methods to achieve higher-quality results. This project provides hands-on experience in data handling, preprocessing, and experimental validation within real scientific workflows.
Required knowledge & skills: Basic Python programming; script automation; familiarity with data processing or machine learning libraries; interest in scientific or medical data analysis.
Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Phone: 519-888-4567 x30481
Location: Remote/RAC-2101
FPGA Acceleration of Deep Learning Recommendation Models
Deep learning recommendation models (DLRMs) are a key workload for many service providers such as Alibaba, Amazon, Google, Meta, and Netflix. They are used to recommend products you might like on online retail websites, suggest shows to watch next on streaming services, or predict the click-through rates for online ads and news feeds. In this project, you will first learn about the DLRM computational workload and then design specialized hardware for accelerating it on one or multiple field-programmable gate arrays (FPGAs). Prior experience of digital hardware design, Verilog or SystemVerilog, and RTL simulation is necessary. Familiarity with FPGA architecture and CAD is a plus.
Supervisor: Prof. Andrew Boutros
Email: andrew.boutros@uwaterloo.ca
Hardware Architecture for Machine Learning Algorithms Based on Stochastic Computing
In this project, you will implement an RTL hardware architecture for a convolutional neural network (CNN) based on stochastic computing, which offers compact arithmetic node implementation, to be applied to problems in medical imaging. The target will be a field-programmable gate array (FPGA) platform.
Supervisor: Prof. Vincent Gaudet
Email: vcgaudet@uwaterloo.ca
Integrated Circuit Design for Cyrogenic and Quantum Computing Applications
This project aims to develop high-speed, low-noise integrated circuits for highly integrated and scalable quantum computer systems. It involves behavioral modeling and transistor-level analog/mixed-signal/RF integrated circuit design using advanced technologies, including GlobalFoundries 22nm FDX-SOI and 12nm FinFET technology. An NDA is required to access these technologies. Students will have the opportunity to participate in a tape-out.
Required Knowledge & Skills:
- Analog/mixed-signal/RF integrated circuit design
- Signal processing
- Cadence
- Python, or MATLAB
Supervisor: Prof. Shiyu Su
Email: shiyu.su@uwaterloo.ca
Numerical Simulations of nanoelectronic devices
This project aims to perform numerical simulations of nanoelectronic devices based on nanomaterials, which will explain experimental device performance.
Required knowledge & skills: Semiconductor device physics
Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Location: Remote/RAC-2101
Wideband Analog-to-Digital Converter (ADC) Design
This project focuses on developing various integrated circuit building blocks for a wideband analog-to-digital converter (ADC) for blocker-resilient wireless communication. The design includes sub-ADCs, digital-to-analog converters, analog filters, and digital signal processing units. The workflow covers behavioral modeling, schematic implementation, layout, and post-layout optimizations using GlobalFoundries 22nm FDX-SOI technology. An NDA is required to access this technology. Students will have the opportunity to participate in a tape-out.
Required Knowledge & Skills:
• Strong understanding of analog and mixed-signal integrated circuits.
• Experience with Cadence and MATLAB.
Supervisor: Prof. Shiyu Su
Email: shiyu.su@uwaterloo.ca