Software-Driven TinyML Hardware Co-Design for AI Power Smart Devices
Explore software-hardware co-design for efficient TinyML systems, focusing on processing-in-memory accelerators to reduce power consumption and enhance AI inference in smart devices.
On-Device Continuous Event-Driven Deep Learning to Avoid Model Drift
Explore on-device continuous event-driven deep learning to prevent model drift in AIoT applications, focusing on real-time data adaptation without user intervention for industrial use cases.
tinyML Trailblazers: Success Stories with Massimo Banzi - Arduino's Journey and Vision
Explore Arduino's journey, from its origins to its impact on machine learning and global tech education, with insights from co-founder Massimo Banzi on innovation and future directions.
Optimizing Data-Flow in Binary Neural Networks
Explore optimizing data flow in Binary Neural Networks, focusing on hardware efficiency, quantization techniques, and accuracy improvements for embedded systems.
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.
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.
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.
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.
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.
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.
Acceleration of Deep Learning Inference on Raspberry Pi's VideoCore GPU
Explore techniques for accelerating deep learning inference on Raspberry Pi's VideoCore GPU, including optimized tools, libraries, and compilers for enhanced performance without compromising accuracy.