SensiML’s Wizard Wand Smart Toy Application
An example of a complete smart toy application built around on-device, ML-based gesture recognition using the M5StickC PLUS.
An example of a complete smart toy application built around on-device, ML-based gesture recognition using the M5StickC PLUS.
We’ve updated SensiML Analytics Toolkit with a variety of new features to make it easier than ever before for users to create and manage their AI/ML projects and visualize their datasets in insightful new ways.
Watch the video of Chris Rogers’ conversation with Ross Sabolcik, VP/GM of Industrial and Commercial IoT products at Silicon Labs, about the benefits of AI at the edge, key use cases, and how Silicon Labs and SensiML are working together to advance IoT smart sensing with ML.
Winner of the Infineon Build AI for the IoT contest selected SensiML for delivering edge AI for their application.
Part 4: Profiling the performance of the AI-accelerated EFR32MG24 model
Part 3: Generating a working smart door lock model for the SiLabs xG24 Dev Kit
Part 2: Collecting smart door lock raw acoustic sensor data and labeling it using SensiML Toolkit
Part 1: Using Silicon Labs xG24 Dev Kit and SensiML Analytics Toolkit, we’ll transform a connected door lock into an innovative enhanced security smart lock.
We often get asked about TinyML – what it is, how it works, how we at SensiML fit into the picture, and for some real-world examples of how it can it useful. Let’s start with the definition. For this we turn to the authoritative body, the tinyML Foundation (tinyml.org). Here’s what they have to say: “Tiny
This week SensiML is releasing an all new version of its industry leading AutoML application SensiML Analytics Studio for building optimized embedded sensor algorithms for IoT devices. SensiML Analytics Studio has existed since our inception as a core component of our AI Toolkit. Previously it consisted of both a Python language interface for data scientists
Artificial Intelligence (AI) is becoming increasingly commonplace as organizations seek to bring effective decision making and operational efficiencies to business in ways that transform how humans and machines work together. As this transformation takes shape, the advantages of edge-based AI implementations over centralized or cloud-based models is changing how AI tools are being deployed and