147 citations · 216 across the 5 of their papers we have counts for
5 papers
Datasheets for Machine Learning Sensors
Matthew Stewart, Yuke Zhang, Pete Warden +12
Machine learning (ML) is becoming prevalent in embedded AI sensing systems. These "ML sensors" enable context-sensitive, real-time data collection and decision-making across divers…
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
Jason Yik, Korneel Van den Berghe, Douwe den Blanken +97
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research fiel…
DataPerf: Benchmarks for Data-Centric AI Development
Mark Mazumder, Colby Banbury, Xiaozhe Yao +42
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithf…
Roofline Model for UAVs: A Bottleneck Analysis Tool for Onboard Compute Characterization of Autonomous Unmanned Aerial Vehicles
Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj +3
We introduce an early-phase bottleneck analysis and characterization model called the F-1 for designing computing systems that target autonomous Unmanned Aerial Vehicles (UAVs). Th…
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers
Colby Banbury, Chuteng Zhou, Igor Fedorov +6
Executing machine learning workloads locally on resource constrained microcontrollers (MCUs) promises to drastically expand the application space of IoT. However, so-called TinyML…