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Found 50 papers, 22 papers with code
TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique security challenges.
Generative AI Agents in Autonomous Machines: A Safety Perspective
The integration of Generative Artificial Intelligence (AI) into autonomous machines represents a major paradigm shift in how these systems operate and unlocks new solutions to problems once deemed intractable.
MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from μWatts to MWatts for Sustainable AI
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.
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
Federated Learning (FL) endeavors to harness decentralized data while preserving privacy, facing challenges of performance, scalability, and collaboration.
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications
Training with Wake Vision improves accuracy by 1. 93% over existing datasets, demonstrating the importance of dataset quality for low-capacity models and dataset size for high-capacity models.
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
By focusing on ``implicitly adversarial'' prompts (those that trigger T2I models to generate unsafe images for non-obvious reasons), we isolate a set of difficult safety issues that human creativity is well-suited to uncover.
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science.
Datasheets for Machine Learning Sensors: Towards Transparency, Auditability, and Responsibility for Intelligent Sensing
Machine learning (ML) sensors are enabling intelligence at the edge by empowering end-users with greater control over their data.
Leveraging Residue Number System for Designing High-Precision Analog Deep Neural Network Accelerators
In addition, we show that RNS can reduce the energy consumption of the data converters within an analog accelerator by several orders of magnitude compared to a regular fixed-point approach.
ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design
The ease of data collection and aggregation in ArchGym facilitates research in ML-aided architecture design space exploration.
Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models
To address this need, we introduce the Adversarial Nibbler challenge.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems.
Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers
This article discusses both the potential of these TinyML applications to address critical sustainability challenges, as well as the environmental footprint of this emerging technology.
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration
It is unclear how scalable single-agent formulations are as we increase the complexity of the design space (e. g., full stack System-on-Chip design).
XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse
We hope that our work will stimulate research and lead to the development of a new generation of ML systems for XR use cases.
Edge Impulse: An MLOps Platform for Tiny Machine Learning
Edge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware targets.
DataPerf: Benchmarks for Data-Centric AI Development
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 faithfulness of the underlying problems.
FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning
Applications of machine learning (ML) are growing by the day for many unique and challenging scientific applications.
Machine Learning Sensors
Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications.
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
Machine learning (ML) has become a pervasive tool across computing systems.
Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles
The processing requirement of autonomous vehicles (AVs) for high-accuracy perception in complex scenarios can exceed the resources offered by the in-vehicle computer, degrading safety and comfort.
FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems
Swarm intelligence is being increasingly deployed in autonomous systems, such as drones and unmanned vehicles.
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference
This enables an online phase where securely computing the result of a nonlinear function requires just a single round of communication, with communication cost equal to twice the number of bits of the input to the nonlinear function.
CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs
In this paper, we present CFU Playground: a full-stack open-source framework that enables rapid and iterative design and evaluation of machine learning (ML) accelerators for embedded ML systems.
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage
The People's Speech is a free-to-download 30, 000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset).
Tabula: Efficiently Computing Nonlinear Activation Functions for Private Neural Network Inference
Multiparty computation approaches to private neural network inference require significant communication between server and client, incur tremendous runtime penalties, and cost massive storage overheads.
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
MLPerf Tiny Benchmark
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications.
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
We show that aggregated model updates in federated learning may be insecure.
Widening Access to Applied Machine Learning with TinyML
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation.
Few-Shot Keyword Spotting in Any Language
With just five training examples, we fine-tune the embedding model for keyword spotting and achieve an average F1 score of 0. 75 on keyword classification for 180 new keywords unseen by the embedding model in these nine languages.
Data Engineering for Everyone
This article shows that open-source data sets are the rocket fuel for research and innovation at even some of the largest AI organizations.
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads
Deep reinforcement learning (RL) has made groundbreaking advancements in robotics, data center management and other applications.
AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs
Balancing a computing system for a UAV requires considering both the cyber (e. g., sensor rate, compute performance) and physical (e. g., payload weight) characteristics that affect overall performance.
MLPerf Mobile Inference Benchmark
This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips and software stacks.
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers
To address this challenge, neural architecture search (NAS) promises to help design accurate ML models that meet the tight MCU memory, latency and energy constraints.
Ranked #1 on
Keyword Spotting
on Google Speech Commands V2 12
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
We introduce TensorFlow Lite Micro (TF Micro), an open-source ML inference framework for running deep-learning models on embedded systems.
Benchmarking TinyML Systems: Challenges and Direction
In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads.
Quantized Neural Network Inference with Precision Batching
We present PrecisionBatching, a quantized inference algorithm for speeding up neural network execution on traditional hardware platforms at low bitwidths without the need for retraining or recalibration.
GLADAS: Gesture Learning for Advanced Driver Assistance Systems
To the best of our knowledge, GLADAS is the first system of its kind designed to provide an infrastructure for further research into human-AV interaction.
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
We believe that this is the first of many future works on enabling computationally energy-efficient and sustainable reinforcement learning.
MLPerf Training Benchmark
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML.
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequately capture the wide data distributions commonly seen in sequence transduction models.
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller
We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter.
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers
Today's cloud service architectures follow a "one size fits all" deployment strategy where the same service version instantiation is provided to the end users.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Deep Reinforcement Learning for Cyber Security
The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever.
Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual Navigation
We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to 40% longer trajectories in one of the environments.
Domain Specific Approximation for Object Detection
There is growing interest in object detection in advanced driver assistance systems and autonomous robots and vehicles.
