Improving MLC PCM Performance through Relaxed Write and Read for Intermediate Resistance Levels. - Saeed Rashidi; Majid Jalili 001; Hamid Sarbazi-Azad (ACM Trans. Archit. Code Optim., 2018)

A Survey on PCM Lifetime Enhancement Schemes. - Saeed Rashidi; Majid Jalili 001; Hamid Sarbazi-Azad (ACM Comput. Surv., 2019)

Scalable Distributed Training of Recommendation Models: An ASTRA-SIM + NS3 case-study with TCP/IP transport. - Saeed Rashidi; Pallavi Shurpali; Srinivas Sridharan 002; Naader Hassani; Dheevatsa Mudigere; Krishnakumar Nair; Misha Smelyanskiy; Tushar Krishna (Hot Interconnects, 2020)

ASTRA-SIM: Enabling SW/HW Co-Design Exploration for Distributed DL Training Platforms. - Saeed Rashidi; Srinivas Sridharan 002; Sudarshan Srinivasan; Tushar Krishna (ISPASS, 2020)

Efficient Communication Acceleration for Next-Gen Scale-up Deep Learning Training Platforms. - Saeed Rashidi; Srinivas Sridharan 002; Sudarshan Srinivasan; Matthew Denton; Tushar Krishna (CoRR, 2020)

Restructuring, Pruning, and Adjustment of Deep Models for Parallel Distributed Inference. - Afshin Abdi; Saeed Rashidi; Faramarz Fekri; Tushar Krishna (CoRR, 2020)

Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms. - Saeed Rashidi; Matthew Denton; Srinivas Sridharan 002; Sudarshan Srinivasan; Amoghavarsha Suresh; Jade Nie; Tushar Krishna (ISCA, 2021)

Exploring Multi-dimensional Hierarchical Network Topologies for Efficient Distributed Training of Trillion Parameter DL Models. - William Won; Saeed Rashidi; Sudarshan Srinivasan; Tushar Krishna (CoRR, 2021)

Impact of RoCE Congestion Control Policies on Distributed Training of DNNs. - Tarannum Khan; Saeed Rashidi; Srinivas Sridharan 002; Pallavi Shurpali; Aditya Akella; Tushar Krishna (HOTI, 2022)

Themis: a network bandwidth-aware collective scheduling policy for distributed training of DL models. - Saeed Rashidi; William Won; Sudarshan Srinivasan; Srinivas Sridharan 002; Tushar Krishna (ISCA, 2022)

COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training. - Divya Kiran Kadiyala; Saeed Rashidi; Taekyung Heo; Abhimanyu Rajeshkumar Bambhaniya; Tushar Krishna; Alexandros Daglis (CoRR, 2022)

Efficient Distributed Inference of Deep Neural Networks via Restructuring and Pruning. - Afshin Abdi; Saeed Rashidi; Faramarz Fekri; Tushar Krishna (AAAI, 2023)

ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale. - William Won; Taekyung Heo; Saeed Rashidi; Srinivas Sridharan 002; Sudarshan Srinivasan; Tushar Krishna (ISPASS, 2023)

Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces. - Srinivas Sridharan 002; Taekyung Heo; Louis Feng; Zhaodong Wang; Matt Bergeron; Wenyin Fu; Shengbao Zheng; Brian Coutinho; Saeed Rashidi; Changhai Man; Tushar Krishna (CoRR, 2023)

HW-SW Methods for Modeling and Optimizing Communication for Scalable Training of Deep Learning Models. - Saeed Rashidi (2023)

Leveraging Memory Expansion to Accelerate Large-Scale DL Training. - Divya Kiran Kadiyala; Saeed Rashidi; Taekyung Heo; Abhimanyu Bambhaniya; Tushar Krishna; Alexandros Daglis (ISPASS, 2024)

LIBRA: Enabling Workload-Aware Multi-Dimensional Network Topology Optimization for Distributed Training of Large AI Models. - William Won; Saeed Rashidi; Sudarshan Srinivasan; Tushar Krishna (ISPASS, 2024)

FRED: Flexible REduction-Distribution Interconnect and Communication Implementation for Wafer-Scale Distributed Training of DNN Models. - Saeed Rashidi; William Won; Sudarshan Srinivasan; Puneet Gupta 001; Tushar Krishna (CoRR, 2024)

FRED: A Wafer-scale Fabric for 3D Parallel DNN Training. - Saeed Rashidi; William Won; Sudarshan Srinivasan; Puneet Gupta 001; Tushar Krishna (ISCA, 2025)