TinyML AutoML Tutorial: Building Production-ready Models with Imagimob AI
Explore Imagimob AI platform for creating production-ready ANN models for time-series data on resource-constrained devices. Learn data visualization, preprocessing, model training, and evaluation techniques.
TinyML Auto ML Deep Dive: GAPflow - From TFLite and ONNX to Optimized C Code
Explore GAPflow: transform neural networks into optimized C code for ultra-low-power GAP processors. Learn quantization, validation, and memory allocation for efficient AI deployment on energy-constrained devices.
TinyML Auto ML Deep Dive: Streaming Analytics for Edge Devices
Explore streaming analytics for edge devices with Stream Analyze's platform. Learn to develop, deploy, and monitor edge AI solutions using real-time data processing and on-device intelligence.
On-Device Learning Under 256KB Memory - Challenges and Solutions for IoT Devices
Explore on-device learning for IoT devices with limited memory. Learn techniques for efficient model adaptation, quantization-aware scaling, and sparse updates to enable continuous learning on tiny hardware.
TinyML Talks South Africa - What Is TinyML?
Explore tinyML applications, tools, and resources in South Africa. Learn about this emerging technology's potential and engage in community discussions for future workshops and events.
State of the TinyAutoML Market - Keynote
Explore the emerging tinyAutoML market, its challenges, opportunities, and potential impact on IoT and edge computing. Gain insights from industry experts on automating tiny machine learning applications.
tinyML Trailblazers: Success Stories with Mouna Elkhatib
Explore tinyML success with Mouna Elkhatib, AONDevices CEO. Learn about voice processing innovations, entrepreneurial challenges, and future trends in edge AI and IoT technologies.
tinyML Trailblazers - Success Stories in TinyML with Thierry Moreau
Explore tinyML success stories with Thierry Moreau, discussing OctoML's journey, Apache TVM, deployment challenges, and the future of edge AI in an interactive conversation.
Sensors and Machine Learning: Waking Smarter for Less
Explore how machine learning optimizes sensor systems, reducing resource usage while improving response. Learn about smart wake-up strategies and efficient data conversion at the edge.
Sensing Applications as a Driver for TinyML Solutions
Explore intelligent MEMS applications in motion learning, sports analytics, and environmental sensing. Learn how domain knowledge optimizes TinyML for ultra-low power contexts and shapes emerging ecosystems.
1 kB and Not a Bit More - The Ideal Weight for a TinyML Model
Explore automated creation of ultra-compact ML models for tiny smart devices, enabling efficient embedding in memory-constrained hardware without data science expertise.
Optimizing AutoML for the tinyML Future
Optimizing AutoML for tinyML: Balancing accuracy, latency, and power efficiency through hardware-aware techniques and full signal flow optimization from sensor to inference.
EON Tuner - AutoML for Constrained Devices in TinyML
Explore AutoML for constrained devices with EON Tuner, optimizing tinyML models through pre-processing, DSP, and ML combinations while considering device limitations and real-world performance.
Suitability of TinyML for Predictive Maintenance in High-Tech Manufacturing
Explore how TinyML and edge analytics predict equipment failures in high-tech manufacturing, reducing downtime and increasing productivity through SensiML and Adapdix EdgeOps integration.
Dissecting a Low Power AI/ML Edge Application - Noise Suppression
Explore noise suppression in AI/ML edge applications, focusing on energy-efficient implementation for battery-powered devices and software frameworks for optimal user experience.
Tiny Models with Big Appetites: Cultivating the Perfect Data Diet for Computer Vision
Explore techniques for curating optimal training datasets and designing sampling strategies for tiny computer vision models, emphasizing data quality and model robustness in real-world environments.
TinyML for All: Full-stack Optimization for Diverse Edge AI Platforms
Full-stack optimization for edge AI platforms, addressing challenges in model compression, neural architecture rebalancing, and design primitives to deploy real-world AI on tiny microcontroller units despite limited resources.
TinyM2Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices
Explore a flexible multimodal learning framework for tiny devices, featuring depth separable layers and mixed precision quantization, with applications in COVID detection and object recognition.
Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks
Explore power-of-two quantization for efficient neural networks, focusing on low bitwidth and hardware compatibility. Learn about key problems, methods, and results in this cutting-edge research.
Improving Energy Efficiency and Robustness of tinyML Computer Vision Using Log-Gradient Input Images
Explore innovative techniques to enhance energy efficiency and robustness in tinyML computer vision, focusing on log-gradient input images and architecture optimization for microcontrollers.
tinyMAN - Lightweight Energy Manager Using Reinforcement Learning for Energy Harvesting Wearable IoT Devices
Explore reinforcement learning for energy management in IoT wearables, focusing on tinyMAN's lightweight approach to achieve energy-neutral operation in harvesting devices.