IWLS 2026

Registration Category

Early Registration
(On or before May 20)

Late Registration
(After May 20)

Student

$300

$400

IEEE/ACM Member

$400

$500

Non-IEEE/ACM Member

$500

$600

IEEE/ACM Life Member

$220

$300

AI-Hardware Co-Design: Advancing Quality, Productivity, and Reliability
Deming Chen, University of Illinois at Urbana-Champaign, USA

Abstract:
AI workloads, from edge intelligence to large language models (LLMs), are placing unprecedented demands on computing systems, exposing fundamental challenges in efficiency, productivity, and reliability, making AI-hardware (AI-HW) co-design essential. This talk presents a unified view of AI-HW co-design across models, synthesis, architectures, and systems, demonstrating how coupling AI algorithms with hardware specialization reshapes performance, energy efficiency, and system robustness. It highlights the A3C3 (AI Algorithm-Accelerator Co- design, Co-search, and Co-generation) methodology, realized through FPGA- and GPU-based systems such as SkyNet, a co-designed model that won double championships at DAC 2019, and extends these principles to LLMs through works like Medusa, SnapKV, and StreamTensor for improved latency and energy efficiency. It further explores reliability through Proof2Silicon, integrating LLMs, formal verification, and hardware synthesis to bridge natural language specifications and silicon implementations. Together, these efforts illustrate how end-to-end AI-HW co-design provides a scalable framework for advancing quality, productivity, and reliability in next-generation AI systems.

Speaker bio:
Deming Chen is the Abel Bliss Professor in the Grainger College of Engineering at the University of Illinois Urbana-Champaign. His research interests include machine learning and AI, system-level design methodologies, hybrid cloud systems, security and confidential computing, and reconfigurable and heterogeneous computing. He has published more than 300 research papers, received 10 Best Paper Awards, 2 ACM/SIGDA TCFPGA Hall-of-Fame Paper Awards, 5 Best Poster Awards, and delivered more than 170 invited talks, including over 20 keynote and distinguished lectures. His research has generated substantial impact, with several open-source solutions adopted by industry (e.g., FCUDA, DNNBuilder, CSRNet, SkyNet, ScaleHLS, Medusa). Dr. Chen is an ACM Fellow, an IEEE Fellow, and previously served as Editor-in-Chief of ACM Transactions on Reconfigurable Technology and Systems (TRETS). Under his leadership, the impact factor of ACM TRETS has increased by 3.8 times. He serves as the Illinois Director of the IBM-Illinois Discovery Accelerator Institute and the Director of the AMD Center of Excellence. Additionally, he has been involved in several startup companies, including AutoESL and Inspirit IoT. He received his Ph.D. in Computer Science from UCLA in 2005.

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