Advancing emerging computing systems through innovations in Edge AI, quantum computing, silicon photonics, and hardware acceleration.
Architectures and systems spanning from cloud to edge for diverse and emerging applications
Edge AI 
Network on Chip
QuantumCo-design across abstraction levels
Devices, circuits and integration technologies enabling future computing
Photonics 
Non-Volatile
Reconfigurable
Energy Efficiency & Sustainability 
Scalability & Heterogeneity 
Open Science & Collaboration The Laboratory for Emerging Computing Systems (LECS) develops methodologies that leverage emerging technologies to design the next generation of computing systems. By bridging device-level innovations and system-level architectures, LECS promotes a cross-layer co-design approach spanning multiple abstraction levels.
Research activities are centered around three complementary pillars: modeling, simulation, and design space exploration. These methodologies enable the rapid evaluation and optimization of emerging technologies, architectures, and applications with respect to performance, energy efficiency, scalability, and implementation cost.
LECS investigates both bottom-up and top-down design methodologies. Bottom-up approaches explore how novel devices and integration technologies can enable new computing paradigms, while top-down approaches derive architectural and technological requirements from application and system-level objectives.
These methodologies have been applied to a broad range of technologies and domains, including photonic and quantum computing, non-volatile memories, network-on-chip architectures, graph accelerators, optical neural networks, reconfigurable and approximate computing, edge AI, and cyber-physical systems. Through collaborations with academic and industrial partners, LECS contributes to the development of efficient, scalable, and sustainable computing platforms for emerging applications.
Sébastien Le Beux received the Ph.D. degree in computer science from the University of Sciences and Technology of Lille, Lille, France, in 2007.
Our group presents three works at the IEEE NEWCAS 2026 conference in Chicoutimi, Québec (June 21–24, 2026): Jean-Baptiste Waring and Milad Eslaminia present their respective works on quantum computing, and Masoud Rahimi presents his work on graph accelerators.
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Ismael Barzani and team Qortex won first place in the Quantum Machine Learning track at the Mil'HaQ Fest 2025 hackathon, hosted by Mila — Quebec AI Institute, applying variational quantum algorithms and parametrized circuits to options-pricing forecasting.
Learn more ↗Filter by year, topic, author, or search across titles and venues. The supervisor's name is highlighted in each row.
We are actively recruiting motivated students at the MSc and PhD levels to work on hardware architectures using emerging technologies. Candidates with backgrounds in computer architecture, hardware programming (VHDL/Verilog), system-level simulation, optimization, or neural networks are particularly encouraged to apply.
Members of equity-deserving groups are strongly encouraged to apply. Send a CV, transcripts, and a brief statement of motivation to sebastien.lebeux (at) concordia.ca.
