595 citations · 1.9k across the 91 of their papers we have counts for
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TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
Jacob Huckelberry, Yuke Zhang, Allison Sansone +3
Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique securit…
MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Arya Tschand, Arun Tejusve Raghunath Rajan, Sachin Idgunji +23
Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmark…
The Magnificent Seven Challenges and Opportunities in Domain-Specific Accelerator Design for Autonomous Systems
Sabrina M. Neuman, Brian Plancher, Vijay Janapa Reddi
The end of Moore's Law and Dennard Scaling has combined with advances in agile hardware design to foster a golden age of domain-specific acceleration. However, this new frontier of…
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
Jeffrey Ma, Alan Tu, Yiling Chen +1
Federated Learning (FL) endeavors to harness decentralized data while preserving privacy, facing challenges of performance, scalability, and collaboration. Asynchronous Federated L…
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications
Colby Banbury, Emil Njor, Andrea Mattia Garavagno +7
Tiny machine learning (TinyML) co-locates models with sensors on microcontrollers, where small models (which are disproportionately sensitive to label noise) and bespoke binary tas…
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
Jessica Quaye, Alicia Parrish, Oana Inel +12
With the rise of text-to-image (T2I) generative AI models reaching wide audiences, it is critical to evaluate model robustness against non-obvious attacks to mitigate the generatio…