by Chris Knorowski, September 24, 2018
"SensiML brings real-time event detection to the sensor endpoint with a platform that is accessible to any application developer. Algorithms are created by feeding labeled sets into a cloud-based analytic engine, where SensiML’s AI routines create an optimized device-ready algorithm that balances the desired accuracy with the resource constraints of the target hardware. These algorithms are automatically compiled to optimized machine code that can run in real time on the target embedded platform. SensiML’s platform brings the firmware and data science expertise so that you can go from PoC to production rapidly and with confidence."
by Chris Rogers, April 10, 2020
This comprehensive overview defines the smart edge AI approach for building IoT sensing applications and explains the many benefits of creating intelligent endpoints. It then discusses automated machine learning workflows (known as “AutoML”) and reviews the key stages of the development process from modeling to prototyping. Most importantly, the paper also gives developers new to AI and machine learning practical guidance and examples for implementing designs using AutoML tools.
by Chris Rogers, April 29, 2020
"Internet of Things (IoT) endpoints have, until very recently, been focused on acquiring raw sensor data and feeding the associated data back towards the “core” of the network for processing. The network’s core data processing resources, whether local or cloud connected, have traditionally been the focus for analyzing the data to identify meaningful events and making decisions based upon the results. As Artificial Intelligence (AI) has become more prominent for sensor data analysis, these same centralized processing resources would seem to be the obvious place for this processing to be implemented."
by Chris Rogers, June 28, 2018
"While much has been made of AI and ML analytics in the cloud and in edge computing, the overlooked opportunity for analytics contributions at the extreme edge (i.e. the sensor processor itself) remains largely untapped. This presentation - delivered at Sensors Expo 2018 in San Jose, CA - delves into the current and forthcoming advancements in extreme edge processing as part of an overall IoT network solution and the benefits and challenges of a more active role for sensor analytics processing."
by Marcellino Gemelli, October 13, 2017
"Counter intuitively, it is often more efficient to simply leave a sensor on permanently, waiting to identify useful information, e.g. an accelerometer in a step counter application. Our sensor system must intelligently determine which data is worth transferring to the cloud and thereby efficiently utilize the available bandwidth and power. The key is for local on-sensor processing to discard most of this superfluous data autonomously and thus save valuable system driver capacities."
Webinar: Endpoint AI Without Writing Code
Industrial Machinery Monitoring Demo
Industrial Wearable Demo
Educational Wearable Demo
Quick Start Video Tutorials
Mando-Hella Electronics Rapidly Develops Smart Sensor Applications for New Markets with SensiML Analytics Toolkit
"With the combination of hardware know-how and SensiML Analytics Toolkit for automating the development of smart sensor algorithms, MHE has developed no fewer than five completely unique intelligent products in less than 9 months’ time. The significantly improvement productivity allows MHE to quickly iterate on product features and applications and ensures they have a competitive advantage in expanding their existing business into new markets."