One paper is accepted to ISCA 2021
G. Lee et al., “CryoGuard: A Near Refresh-Free Robust DRAM Design for Cryogenic Computing”, ISCA 2021
G. Lee et al., “CryoGuard: A Near Refresh-Free Robust DRAM Design for Cryogenic Computing”, ISCA 2021
I. Byun, D. Min et al., “A Next-Generation Cryogenic Processor Architecture”, IEEE Micro’s Top Picks 2021
K. Ishida, I. Byun et al., “Superconductor Computing for Neural Networks”, IEEE Micro’s Top Picks 2021
J. Hur et al., “DiFuzzRTL: Differential Fuzz Testing to Find CPU Bugs”, S&P 2021
H. Lee et al., “NeuroEngine: A Hardware-based Event-driven Simulation System for Advanced Brain-inspired Computing”, ASPLOS 2021
D. Kwon et al., “FVM: FPGA-assisted Virtual Device Emulation for Fast, Scalable, and Flexible Storage Virtualization”, OSDI 2020
P. Park et al., “TrainBox: An Extreme-Scale Neural Network Training Server Architecture by Systematically Balancing Operations”, MICRO 2020
K. Ishida, I. Byun et al., “SuperNPU: An Extremely Fast Neural Processing Unit Using Superconducting Logic Devices”, MICRO 2020
I. Byun et al., “CryoCore: A Fast and Dense Processor Architecture for Cryogenic Computing”, ISCA 2020
E. Baek et al., “A Multi-Neural Network Acceleration Architecture”, ISCA 2020
D. Kwon et al., “Scalable Multi-FPGA Acceleration for Large RNNs with Full Parallelism Levels”, DAC 2020
D. Min et al., “CryoCache: A Fast, Large, and Cost-Effective Cache Architecture for Cryogenic Computing”, ASPLOS 2020 (to appear)
M. Ajdari et al., “FIDR: A Scalable Storage System for Fine-Grain Inline Data Reduction with Efficient Memory Handling”, MICRO 2019
E. Baek et al., “FlexLearn: Fast and Highly Efficient Brain Simulations Using Flexible On-Chip Learning”, MICRO 2019