CryoCore: A Fast and Dense Processor Architecture for Cryogenic Computing
Ilkwon Byun, Dongmoon Min, Gyu-hyeon Lee, Seongmin Na, Jangwoo Kim
摘要
Cryogenic computing can achieve high performance and power efficiency by dramatically reducing the device's leakage power and wire resistance at low temperatures. Recent advances towards cryogenic computing focus on developing cryogenic-optimal cache and memory devices to overcome memory capacity, latency, and power walls. However, little research has been conducted to develop a cryogenic-optimal core architecture despite its high potentials in performance, power, and area efficiency. Once a cryogenic-optimal core becomes available, it will also take full advantage of the cryogenic-optimal cache and memory devices, which leads to a cryogenic-optimal computer.In this paper, we first develop CryoCore-Model (CC-Modet), a cryogenic processor modeling framework which can accurately estimate the maximum clock frequency of processor models running at 77K. Next, driven by the modeling tool, we design CryoCore, a 77K-optimal core microarchitecture to maximize the core's performance and area efficiency while minimizing the cooling cost. The key idea of CryoCore is to architect a core in a way to reduce the size and number of cooling-unfriendly microarchitecture units and maximize the potential of a voltage and frequency scaling at 77K. Finally, we propose two halfsized, but differently voltage-scaled CryoCore designs aiming for either the maximum performance or power efficiency. With both conventional and our design integrated with cryogenic memories, our high-performance CryoCore design achieves 41% higher single-thread performance for the same power budget and 2x higher multi-thread performance for the same die area. Our lowpower CryoCore design reduces the power cost by 38% without sacrificing the single-thread performance.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
引用它的顶会 Paper2
- MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks TrainingHongwu Peng, Xi Xie, Kaustubh Shivdikar, Md Amit Hasan 等ASPLOS 2024 · 被引用 32 次
- SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson DevicesZhengang Li, Geng Yuan, Tomoharu Yamauchi, Masoud Zabihi 等MICRO 2023 · 被引用 7 次
相关 Paper
- CryoWire: wire-driven microarchitecture designs for cryogenic computingDongmoon Min, Yujin Chung, Ilkwon Byun, Junpyo Kim 等ASPLOS 2022 · 被引用 10 次
- CryoCache: A Fast, Large, and Cost-Effective Cache Architecture for Cryogenic ComputingDongmoon Min, Ilkwon Byun, Gyu-hyeon Lee, Seongmin Na 等ASPLOS 2020 · 被引用 36 次
- CryoGuard: A Near Refresh-Free Robust DRAM Design for Cryogenic ComputingGyu-hyeon Lee, Seongmin Na, Ilkwon Byun, Dongmoon Min 等ISCA 2021 · 被引用 28 次
- SuperCore: An Ultra-Fast Superconducting Processor for Cryogenic ApplicationsJunhyuk Choi, Ilkwon Byun, Juwon Hong, Dongmoon Min 等MICRO 2024 · 被引用 9 次
- Design Automation for Cryogenic CMOS CircuitsVictor M. van Santen, Marcel Walter, Florian Klemme, Shivendra Singh Parihar 等DAC 2023 · 被引用 12 次
