Assistant Professor Peipei Zhou was honored with the 10-Year Retrospective Most Influential Paper Award at the 2025 Institute of Electrical and Electronics Engineers (IEEE)/Association for Computing Machinery (ACM) International Conference on Computer-Aided Design (ICCAD), held in Munich, Germany in October. The award recognizes the lasting impact of the 2016 paper, “Caffeine: Towards uniformed representation and acceleration for deep convolutional neural networks,” co-authored by Zhou with her Ph.D. adviser Jason Cong (UCLA), Chen Zhang (Shanghai Jiao Tong University), Zhenman Fang (Simon Fraser University), and Peichen Pan (Falcon-Computing Solutions).
The paper presented Caffeine, a full-stack design automation tool that synthesizes deep neural networks specifically onto field-programmable gate-arrays (FPGA). At the hardware level, it automatically extracts operator sets from AI models, optimally maps them to a reconfigurable intermediate representation, and generates efficient FPGA hardware implementations. At the software level, it automatically generates instruction streams based on the hardware configuration (matrix engines, caches, pipelines, etc.), optimizing operator scheduling and memory management.
Deep neural networks are built from distinct computational stages called layers. Before Caffeine, most FPGA accelerators focused only on the convolution layers, which limited performance at the fully-connected layers. Caffeine introduced a novel unified convolutional representation to efficiently accelerate the entire network on a single FPGA. This approach included key techniques like automated memory access transformation to solve the critical challenge of optimizing for the limited memory bandwidth of FPGAs. The paper’s lasting influence is highlighted by its nearly 800 citations, including from nearly every major company in the AI hardware acceleration industry such as AMD, Google, Intel, and Nvidia.
Zhou joined Brown Engineering in the fall of 2024 from the University of Pittsburgh. She received her Ph.D. in computer science (2019) and M.S. in electrical and computer engineering (2014) from UCLA, and her B.S. in electrical and computer engineering (2012) from Southeast University in China. Her research investigates customized computer architecture, programming abstraction, and electronic design automation for applications including healthcare, e.g., precision medicine, and artificial intelligence.
She has published in top-tier IEEE/ACM computer system and electronic design automation conferences and journals including: the Field-Programmable Gate Array conference, the Field-Programmable Custom Computing Machines conference, the Design Automation Conference, the ICCAD, the International Symposium on Performance Analysis of Systems and Software (ISPASS), the Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) journal, the Transactions on Embedded Computer Systems journal, the Transactions on Design Automation of Electronic Systems journal, and IEEE Micro. Her work won the 2019 IEEE TCAD Donald O. Pederson Best Paper Award. Other awards include the 2024 ACM/IEEE International Green and Sustainable Computing Best Viewpoint Paper, 2024 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays Best Paper Nominee, the 2018 IEEE ISPASS Best Paper Nominee, and the 2018 IEEE/ACM ICCAD Best Paper Nominee.