Y
Yunqi Zhang
Researcher at University of Michigan
Publications - 23
Citations - 1234
Yunqi Zhang is an academic researcher from University of Michigan. The author has contributed to research in topics: Server & Virtual machine. The author has an hindex of 11, co-authored 21 publications receiving 964 citations.
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Proceedings ArticleDOI
The Architectural Implications of Autonomous Driving: Constraints and Acceleration
TL;DR: With accelerator-based designs, this work is able to build an end-to-end autonomous driving system that meets all the design constraints, and explore the trade-offs among performance, power and the higher accuracy enabled by higher resolution cameras.
Proceedings ArticleDOI
Sirius: An Open End-to-End Voice and Vision Personal Assistant and Its Implications for Future Warehouse Scale Computers
Johann Hauswald,Michael A. Laurenzano,Yunqi Zhang,Cheng Li,Austin Rovinski,Arjun Khurana,Ronald G. Dreslinski,Trevor Mudge,Vinicius Petrucci,Lingjia Tang,Jason Mars +10 more
TL;DR: The design of Sirius is presented, an open end-to-end IPA web-service application that accepts queries in the form of voice and images, and responds with natural language, and finds that accelerators are critical for the future scalability of IPA services.
Proceedings ArticleDOI
SMiTe: Precise QoS Prediction on Real-System SMT Processors to Improve Utilization in Warehouse Scale Computers
TL;DR: This paper demonstrates through a real- system investigation that the fundamental difference between resource sharing behaviors on CMP and SMT architectures calls for a redesign of the way the authors model interference, and proposes SMiTe, a methodology that enables precise performance prediction for SMT co-location on real-system commodity processors.
Proceedings ArticleDOI
Adrenaline: Pinpointing and reining in tail queries with quick voltage boosting
Chang-Hong Hsu,Yunqi Zhang,Michael A. Laurenzano,David Meisner,Thomas F. Wenisch,Jason Mars,Lingjia Tang,Ronald G. Dreslinski +7 more
TL;DR: This work proposes Adrenaline, an approach to leverage finer granularity, 10's of nanoseconds, voltage boosting to effectively rein in the tail latency with query-level precision and demonstrates the effectiveness of the methodology under various workload configurations.
Proceedings ArticleDOI
Octopus-Man: QoS-driven task management for heterogeneous multicores in warehouse-scale computers
Vinicius Petrucci,Michael A. Laurenzano,John Doherty,Yunqi Zhang,Daniel Mosse,Jason Mars,Lingjia Tang +6 more
TL;DR: Octopus-Man is presented, a novel QoS-aware task management solution that dynamically maps latency-sensitive tasks to the least power-hungry processing resources that are sufficient to meet the QoS requirements.