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publications
HV 2 M: A novel approach to boost inter-VM network performance for Xen-based HVMs.
Published in Journal of Systems and Software (JSS), 2016
Recommended citation: Cheng, Kun, Yuebin Bai, Yongwang Zhao, Yao Ma, Duo Lu, Yuanfeng Peng, and Minxuan Zhou. “HV 2 M: A novel approach to boost inter-VM network performance for Xen-based HVMs.” Journal of Systems and Software 114 (2016): 54-68.
GAS: A Heterogeneous Memory Acceleration for Graph Processing
Published in IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2018
Recommended citation: Minxuan Zhou, Mohsen Imani, Saransh Gupta, and Tajana Rosing, “GAS: A Heterogeneous Memory Acceleration for Graph Processing”, IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2018.
GRAM: Graph Processing in a ReRAM-based Computational Memory
Published in 24th Asia and South Pacific Design Automation Conference (ASP-DAC), 2019
Recommended citation: Minxuan Zhou, Mohsen Imani, Saransh Gupta, Yeseong Kim, and Tajana Rosing, “GRAM: Graph Processing in a ReRAM-based Computational Memory”, 24th Asia and South Pacific Design Automation Conference (ASP-DAC), 2019
Thermal-Aware Design and Management for Search-based In-Memory Acceleration
Published in Design Automation Conference (DAC), 2019
Recommended citation: Minxuan Zhou, Mohsen Imani, Saransh Gupta, and Tajana Rosing, “Thermal-Aware Design and Management for Search-based In-Memory Acceleration”, Design Automation Conference (DAC), 2019.
Temperature-Aware DRAM Cache Management -Relaxing Thermal Constraints in 3D Systems
Published in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2019
Recommended citation: Minxuan Zhou, Andreas Prodromou, Rui Wang, Hailong Yang, Depei Qian, Dean Tullsen. “Temperature-Aware DRAM Cache Management -Relaxing Thermal Constraints in 3D Systems”. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2019
HR3AM: A Heat Resilient Design for RRAM-based Neuromorphic Computing
Published in ACM/IEEE International Symposium on Low Power Electronics and Design , 2019
Recommended citation: Xiao Liu, Minxuan Zhou, Tajana Rosing, and Jishen Zhao. 2019. HR3AM: A Heat Resilient Design for RRAM-based Neuromorphic Computing. accepted in ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2019
DigitalPIM: Digital-based Processing In-Memory for Big Data Acceleration
Published in ACM Proceedings of the 2019 on Great Lakes Symposium on VLSI (GLSVLSI), 2019
Recommended citation: Mohsen Imani, Saransh Gupta, Yeseong Kim, Minxuan Zhou, and Tajana Rosing. DigitalPIM: Digital-based Processing In-Memory for Big Data Acceleration. ACM Proceedings of the 2019 on Great Lakes Symposium on VLSI
DPSim: A Full-stack Simulation Infrastructure for Digital Processing In-Memory Architecture
Published in 26th Asia and South Pacific Design Automation Conference (ASP-DAC), 2021
Recommended citation: Minxuan Zhou, Mohsen Imani, Yeseong Kim, Saransh Gupta, and Tajana Rosing, “DPSim: A Full-stack Simulation Infrastructure for Digital Processing In-Memory Architecture”, 26th Asia and South Pacific Design Automation Conference (ASP-DAC), 2021
HyGraph: Accelerating Graph Processing with Hybrid Memory-centric Computing
Published in Design, Automation and Test in Europe Conference (DATE), 2021
Recommended citation: Minxuan Zhou, Muzhou Li, Mohsen Imani, and Tajana Rosing, “HyGraph: Accelerating Graph Processing with Hybrid Memory-centric Computing”, Design, Automation and Test in Europe Conference (DATE), 2021
MAT: Processing In-Memory Acceleration for Long-Sequence Attention
Published in Design Automation Conference (DAC), 2021
Recommended citation: Minxuan Zhou, Yunhui Guo, Weihong Xu, Bin Li, Kevin Eliceiri, and Tajana Rosing, “MAT: Processing In-Memory Acceleration for Long-Sequence Attention”, Design Automation Conference (DAC), 2021
Ultra Efficient Acceleration for De Novo Genome Assembly via Near-Memory Computing
Published in International Conference on Parallel Architectures and Compilation Techniques (PACT), 2021
Recommended citation: Minxuan Zhou, Lingxi Wu, Muzhou Li, Niema Moshiri, Kevin Skadron, and Tajana Rosing, “Ultra Efficient Acceleration for De Novo Genome Assembly via Near-Memory Computing”, International Conference on Parallel Architectures and Compilation Techniques (PACT), 2021
PIM-DL: Boosting DNN Inference on Digital Processing In-Memory Architectures via Data Layout Optimizations
Published in International Conference on Parallel Architectures and Compilation Techniques (PACT), 2021
Recommended citation: Minxuan Zhou, Guoyang Chen, Mohsen Imani, Saransh Gupta, Weifeng Zhang, and Tajana Rosing, “PIM-DL: Boosting DNN Inference on Digital Processing In-Memory Architectures via Data Layout Optimizations”, International Conference on Parallel Architectures and Compilation Techniques (PACT), 2021
TransPIM: A Memory-based Acceleration via Software-Hardware Co-Design for Transformers
Published in The 28th IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2022
Recommended citation: Minxuan Zhou*, Weihong Xu*, Jaeyoung Kang, and Tajana Rosing, “TransPIM: A Memory-based Acceleration via Software-Hardware Co-Design for Transformers”, The 28th IEEE International Symposium on High-Performance Computer Architecture (HPCA’22), 2022
PIMProf: An Automated Program Profiler for Processing-in-Memory Offloading Decisions
Published in Design, Automation and Test in Europe Conference (DATE), 2022
Recommended citation: Yizhou Wei, Minxuan Zhou, Sihang Liu, Korakit Seemakhupt, Tajana Rosing and Samira Khan. “PIMProf: An Automated Program Profiler for Processing-in-Memory Offloading Decisions”, Design, Automation and Test in Europe Conference (DATE’22), 2022
Recommended citation: Yizhou Wei, Minxuan Zhou, Sihang Liu, Korakit Seemakhupt, Tajana Rosing and Samira Khan. “PIMProf: An Automated Program Profiler for Processing-in-Memory Offloading Decisions”, Design, Automation and Test in Europe Conference (DATE’22), 2022
RelHD: A Lightweight Graph-based Learning with Hyperdimensional Computing
Published in he 40th IEEE International Conference on Computer Design (ICCD), 2022
Recommended citation: Jaeyoung Kang, Minxuan Zhou, Abhinav Bhansali, Weihong Xu, Anthony Thomas and Tajana Rosing, “RelHD: A Lightweight Graph-based Learning with Hyperdimensional Computing”, The 40th IEEE International Conference on Computer Design (ICCD’22), 2022
OverlaPIM: Overlap Optimization for Processing In-Memory Neural Network Acceleration
Published in Design, Automation and Test in Europe Conference (DATE), 2023
Recommended citation: Minxuan Zhou*, Xuan Wang*, and Tajana Rosing, “OverlaPIM: Overlap Optimization for Processing In-Memory Neural Network Acceleration”, Design, Automation and Test in Europe Conference (DATE’23), 2023
Efficient Machine Learning on Encrypted Data using Hyperdimensional Computing
Published in IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2023
Recommended citation: Yujin Nam, Minxuan Zhou, Saransh Gupta, Gabrielle De Micheli, Rosario Cammarota, Chris Wilkerson, Daniele Micciancio, and Tajana Rosing, “Efficient Machine Learning on Encrypted Data using Hyperdimensional Computing”, IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2023
Abakus: Accelerating k-mer Counting With Storage Technology
Published in ACM Trans. Archit. Code Optim (TACO), 2023
Recommended citation: Lingxi Wu*, Minxuan Zhou*, Weihong Xu, Ashish Venkat, Tajana Rosing, and Kevin Skadron. "Abakus: Accelerating k-mer Counting With Storage Technology". ACM Trans. Archit. Code Optim. https://doi.org/10.1145/3632952
FHEmem: A Processing In-Memory Accelerator for Fully Homomorphic Encryption
Published in Arxiv, 2023
Recommended citation: Minxuan Zhou, Yujin Nam, Pranav Gangwar, Weihong Xu, Arpan Dutta, Kartikeyan Subramanyam, Chris Wilkerson, Rosario Cammarota, Saransh Gupta, and Tajana Rosing. "FHEmem: A Processing In-Memory Accelerator for Fully Homomorphic Encryption". arXiv:2311.16293
PRIMATE: Processing in Memory Acceleration for Dynamic Token-pruning Transformers
Published in 29th Asia and South Pacific Design Automation Conference (ASP-DAC), 2024
Recommended citation: Yue Pan, Minxuan Zhou, Chonghan Lee, Zheyu Li, Rishika Kushwah, Vijaykrishnan Narayanan, and Tajana Rosing, “PRIMATE: Processing in Memory Acceleration for Dynamic Token-pruning Transformers”, 29th Asia and South Pacific Design Automation Conference (ASP-DAC), 2024
HygHD: Hyperdimensional Hypergraph Learning
Published in Design, Automation, and Test in Europe (DATE), 2024
Recommended citation: Jaeyoung Kang, You Hak Lee, Minxuan Zhou, Weihong Xu and Tajana Rosing, “HygHD: Hyperdimensional Hypergraph Learning”, Design, Automation, and Test in Europe (DATE), 2024
RL-PTQ: RL-based Mixed Precision Quantization for Hybrid Vision Transformers
Published in Design Automation Conference (DAC), 2024
Recommended citation: Eunji Kwon, Minxuan Zhou, Weihong Xu, Tajana Rosing and Seokhyeong Kang, “RL-PTQ: RL-based Mixed Precision Quantization for Hybrid Vision Transformers”, Design Automation Conference (DAC), 2024
Fast-OverlaPIM: A Fast Overlap-driven Mapping Framework for Processing In-Memory Neural Network Acceleration
Published in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2024
Recommended citation: Xuan Wang*, Minxuan Zhou*,and Tajana Rosing. “Fast-OverlaPIM: A Fast Overlap-driven Mapping Framework for Processing In-Memory Neural Network Acceleration”. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2024
Multi-Objective Software-Hardware Co-Optimizationfor HD-PIM via Noise-Aware Bayesian Optimization
Published in ACM/IEEE International Conference on Computer-Aided Design (ICCAD), 2024
Recommended citation: Chien-Yi Yang, Minxuan Zhou, Flavio Ponzina, Suraj Sathya Prakash, Raid Ayoub, Pietro Mercati, Mahesh Subedar, and Tajana Rosing, “Multi-Objective Software-Hardware Co-Optimizationfor HD-PIM via Noise-Aware Bayesian Optimization”, ACM/IEEE International Conference on Computer-Aided Design (ICCAD), 2024
UFC: A Unified Accelerator for Fully Homomorphic Encryption
Published in 57th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2024
Recommended citation: Minxuan Zhou, Yujin Nam, Xuan Wang, Youhak Lee, Chris Wilkerson, Raghavan Kumar, Sachin Taneja, Sanu Mathew, Rosario Cammarota, and Tajana Rosing, “UFC: A Unified Accelerator for Fully Homomorphic Encryption”, 57th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2024
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Computer Architecture
Undergraduate course, Illinois Institute of Technology, Computer Science Department, 2025
This course introduces the fundamental principles underlying modern computer architecture; explains the key hardware components, organizations, mechanisms that realize full-functional, high-performance, and energy-efficient computing systems. Covered topics include Instruction Set Architectures, Pipelining, Out-of-order Execution, Branch Prediction, Memory Hierarchy, Parallel Machines, Application-Specific Computer Architecture with an emphasis on Machine Learning Accelerators, and Non-Conventional Computer Architecture.