RoSÉ: A Hardware-Software Co-Simulation Infrastructure Enabling Pre-Silicon Full-Stack Robotics SoC Evaluation
Dima Nikiforov, Shengjun Chris Dong, Chengyi Lux Zhang, Seah Kim, Borivoje Nikolic, Yakun Sophia Shao
摘要
Robotic systems, such as autonomous unmanned aerial vehicles (UAVs) and self-driving cars, have been widely deployed in many scenarios and have the potential to revolutionize the future generation of computing. To improve the performance and energy efficiency of robotic platforms, significant research efforts are being devoted to developing hardware accelerators for workloads that form bottlenecks in the robotics software pipeline. Although domainspecific accelerators can offer improved efficiency over generalpurpose processors on isolated robotics benchmarks, system-level constraints such as data movement and contention over shared resources can significantly impact the achievable end-to-end acceleration. In addition, the closed-loop nature of robotic systems, where there is a tight interaction across different deployed environments, software stacks, and hardware architecture, further exacerbates the difficulties of evaluating robotics SoCs.
To address this limitation, we develop RoSÉ, an open-source, hardware-software co-simulation infrastructure for full-stack, presilicon hardware-in-the-loop evaluation of robotics SoCs, together with the full software stack and realistic environments created to support robotics workloads. RoSÉ captures the complex interactions across hardware, algorithm, and environment, enabling new architectural research directions in hardware-software co-design for robotic systems.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper6
- Accel-Sim: An Extensible Simulation Framework for Validated GPU ModelingMahmoud Khairy, Zhesheng Shen, Tor M. Aamodt, Timothy G. RogersISCA 2020 · 被引用 366 次
- Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack IntegrationHasan Genc, Seah Kim, Alon Amid, Ameer Haj-Ali 等DAC 2021 · 被引用 325 次
- Robomorphic computing: a design methodology for domain-specific accelerators parameterized by robot morphologySabrina M. Neuman, Brian Plancher, Thomas Bourgeat, Thierry Tambe 等ASPLOS 2021 · 被引用 43 次
- MoCA: Memory-Centric, Adaptive Execution for Multi-Tenant Deep Neural NetworksSeah Kim, Hasan Genc, Vadim Vadimovich Nikiforov, Krste Asanovic 等HPCA 2023 · 被引用 36 次
- Quantifying the design-space tradeoffs in autonomous dronesRamyad Hadidi, Bahar Asgari, Sam Jijina, Adriana Amyette 等ASPLOS 2021 · 被引用 32 次
相关 Paper
- Archytas: A Framework for Synthesizing and Dynamically Optimizing Accelerators for Robotic LocalizationWeizhuang Liu, Bo Yu, Yiming Gan, Qiang Liu 等MICRO 2021 · 被引用 41 次
- Automatic Domain-Specific SoC Design for Autonomous Unmanned Aerial VehiclesSrivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj, Paul N. Whatmough 等MICRO 2022 · 被引用 36 次
- Accelerator Polymorphism: Transcending Domain-Specific Architectures with RoboticsHanyang Xu, Seongryong Oh, Yubin Lee, Ashwin Rohit Alagiri Rajan 等ISCA 2026
- ARTEMIS: Agile Discovery of Efficient Real-Time Systems-on-Chips in the Heterogeneous EraSubhankar Pal, Aporva Amarnath, Behzad Boroujerdian, Augusto Vega 等HPCA 2025 · 被引用 2 次
- RoboShape: Using Topology Patterns to Scalably and Flexibly Deploy Accelerators Across RobotsSabrina M. Neuman, Radhika Ghosal, Thomas Bourgeat, Brian Plancher 等ISCA 2023 · 被引用 24 次
