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.
Next-Generation Deep-Learning Accelerators: From Hardware to System - tinyML Summit 2022
Explore next-gen deep learning accelerators, focusing on system-level challenges and opportunities in design, integration, and scheduling for future AI hardware advancements.
Programmable In-Memory Computing Accelerator with 100 SRAM IMC Macros
Explore programmable in-memory computing accelerators for efficient deep neural network processing, featuring 100 SRAM IMC macros and innovative architecture design for improved energy efficiency.
Deploy AI with AWS ML and IoT Services on Jetson Nano
Learn to deploy AI models on Jetson Nano using AWS ML and IoT services. Explore SageMaker, Greengrass, and real-time video processing for edge computing applications.
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.
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.
Accelerate ML Development With Cloud-Based Arm Cortex-M Models
Explore cloud-based Arm Cortex-M models for accelerated ML development, featuring Virtual Hardware integration, Python-scriptable peripherals, and TensorFlow optimizations.
Lightweight Face Detection for Ultra-Low Power AI Processors - tinyML Asia 2021
Lightweight face detection method for ultra-low power AI processors, featuring real-time application and accuracy in various scenarios. Explores implementation on Himax WE-I Plus platform for always-on sensing products.
Building and Enabling Voice Control with ARM Cortex-M
Explore Sensory's Edge-AI technology for creating custom wake words and voice control on ARM Cortex-M, with a focus on VoiceHub platform for developers.
On-Device Neural End-to-End Speech Recognition and Synthesis Algorithms - A Review
Comprehensive review of on-device neural speech recognition and synthesis algorithms, exploring end-to-end models, optimization techniques, and their potential for on-device implementation.
Extremely Low-Bit Quantization for Transformers - tinyML Asia 2021
Explore extremely low-bit quantization techniques for Transformers, focusing on mixed precision strategies to optimize deployment on resource-limited devices without compromising accuracy.
AIfES - An Open-Source Standalone AI Framework for Almost Any Hardware
Explore AIfES, an open-source AI framework for embedded systems, enabling neural network deployment and training on various hardware, including 8-bit microcontrollers and Arduino boards.
Binarized Neural Networks on Microcontrollers
Explore Binarized Neural Networks for efficient deep learning on microcontrollers, enabling complex tasks with minimal memory and high performance using 1-bit encoding for weights and activations.
Embedded Machine Learning in the Real World
Explore embedded ML applications, challenges, and opportunities in real-world scenarios, focusing on tiny models, accelerated hardware, and practical tooling.