Reliable Silicon Systems Lab

McGill downtown campus quad, facing Redpath Museum.

Photo by Negin Firouzian

Reliable Silicon Systems Lab

Department of Electrical and Computer Engineering, McGill University

The Reliabile Silicon Systems Lab (RSSL), in the Department of Electrical and Computer Engineering at McGill University, conducts computer system optimization research targeting a wide variety of applications, from safety-critical automotive and aerospace systems, to machine learning on complex multiprocessors.

Computer system design faces a multitude of challenges today, given the expectations of reliable high performance software, and low-power execution, on affordable hardware. RSSL is dedicated to the development of novel architectures and automation methodologies that support hardware-software co-design and optimization of heterogeneous multiprocessor systems. Recent topics include research on:

  • Computer architecture and automated computer system design
  • Fault-tolerant and safety-critical system design
  • Aerospace and automative system security
  • Hardware-software optimization of machine learning systems

RSSL is directed by Professor Brett H. Meyer, who has more than 15 years of experience in research on the optimization of computer systems; the lab is supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Fonds de recherche du Québec (FRQNT), and, industrial sponsors.

Interested in joining the team? Learn about out what we’re currently looking for in new members.

Quickly browse our past publications here:

News

Prof. Meyer presents at 2022 Edge Intelligence Workshop

Prof. Brett H. Meyer was invited to present at the 2022 Edge Intelligence Workshop (EIW) on September 20. The two day workshop featured a number of prominent researchers working to develop or optimize machine learning algorithms on the resource-constrained devices at the edge of the Internet, accompanied by a wide variety of student posters. Professor Meyer’s talk focused on recent research at the McGill Edge Intelligence Lab on BERT inference latency modeling, and inference and throughput optimization through task pipelining and mapping on the Kirin 970 SoC.

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Profs. Gross and Meyer give deep learning tutorial at IEEE EPEPS 2019

Profs. Warren Gross and Brett H. Meyer presented a tutorial on the optimization of hardare and software for deep learning at IEEE EPEPS 2019 in Montreal today. Gross introduced machine learning in general, and deep learning in particular, from a computational perspective. He then summarized recent work on custom architecture for DNN acceleration. Meyer followed up with an introduction to multi-objective hyperparameter optimization, with a focus on deployment to low-cost IoT processors.

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Prof. Meyer presents at Dawson Humanities and Public Life Conference

Prof. Brett H. Meyer presented at the Dawson College Humanities and Public Life Conference today, giving a talk entitled ‘The Algorithms Aren’t Alright: Why Machine Learning Still Needs Us.’ In it he introduced machine learning in general, deep learning in particular, and highlighted some of the challenges that arise in the application of deep learning: robustness, explainaibility, and bias.

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