97 citations · 327 across the 14 of their papers we have counts for
15 papers
Enhancing Network Management Using Code Generated by Large Language Models
Sathiya Kumaran Mani, Yajie Zhou, Kevin Hsieh +3
Analyzing network topologies and communication graphs plays a crucial role in contemporary network management. However, the absence of a cohesive approach leads to a challenging le…
Enhancing Network Failure Mitigation with Performance-Aware Ranking
Pooria Namyar, Arvin Ghavidel, Daniel Crankshaw +5
Cloud providers install mitigations to reduce the impact of network failures within their datacenters. Existing network mitigation systems rely on simple local criteria or global p…
Federated Learning under Distributed Concept Drift
Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang +2
Federated Learning (FL) under distributed concept drift is a largely unexplored area. Although concept drift is itself a well-studied phenomenon, it poses particular challenges for…
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations
Jinhyun So, Kevin Hsieh, Behnaz Arzani +3
Large-scale deployments of low Earth orbit (LEO) satellites collect massive amount of Earth imageries and sensor data, which can empower machine learning (ML) to address global cha…
Towards a Cost vs. Quality Sweet Spot for Monitoring Networks
Nofel Yaseen, Behnaz Arzani, Krishna Chintalapudi +6
Continuously monitoring a wide variety of performance and fault metrics has become a crucial part of operating large-scale datacenter networks. In this work, we ask whether we can…
Interpret-able feedback for AutoML systems
Behnaz Arzani, Kevin Hsieh, Haoxian Chen
Automated machine learning (AutoML) systems aim to enable training machine learning (ML) models for non-ML experts. A shortcoming of these systems is that when they fail to produce…
Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data
Kevin Hsieh
The usability and practicality of any machine learning (ML) applications are largely influenced by two critical but hard-to-attain factors: low latency and low cost. Unfortunately,…
The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu +1
Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a signific…
Flexible-Latency DRAM: Understanding and Exploiting Latency Variation in Modern DRAM Chips
Kevin K. Chang, Abhijith Kashyap, Hasan Hassan +7
This article summarizes key results of our work on experimental characterization and analysis of latency variation and latency-reliability trade-offs in modern DRAM chips, which wa…
Decoupling GPU Programming Models from Resource Management for Enhanced Programming Ease, Portability, and Performance
Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko +6
The application resource specification--a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block--forms a critica…
Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management
Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko +5
The application resource specification--a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block--forms a critica…
Enabling the Adoption of Processing-in-Memory: Challenges, Mechanisms, Future Research Directions
Saugata Ghose, Kevin Hsieh, Amirali Boroumand +2
Poor DRAM technology scaling over the course of many years has caused DRAM-based main memory to increasingly become a larger system bottleneck. A major reason for the bottleneck is…
Focus: Querying Large Video Datasets with Low Latency and Low Cost
Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik +4
Large volumes of videos are continuously recorded from cameras deployed for traffic control and surveillance with the goal of answering "after the fact" queries: identify video fra…
D-SLATS: Distributed Simultaneous Localization and Time Synchronization
Amr Alanwar, Henrique Ferraz, Kevin Hsieh +4
Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Althou…
LazyPIM: Efficient Support for Cache Coherence in Processing-in-Memory Architectures
Amirali Boroumand, Saugata Ghose, Minesh Patel +7
Processing-in-memory (PIM) architectures have seen an increase in popularity recently, as the high internal bandwidth available within 3D-stacked memory provides greater incentive…