PRODUCT INFORMATION

SensiML Product Brief

Description of the SensiML Toolkit product offering including high level benefits, applications, toolkit components, and supported offerings.

SensiML Toolkit Technical Overview

by Chris Knorowski, September 24, 2018

Full Article

"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." 

USEFUL ARTICLES AND LINKS

Building Smart IoT Devices Faster with AutoML

by Chris Rogers, April 10, 2020

Full Article

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.

The Opportunity for AI at the Edge and Beyond

by Chris Rogers, April 29, 2020

Full Article

"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."

App Note: Supporting Custom Sensors and Embedded Devices

by Chris Rogers, June 24, 2020

Full Article

"SensiML Analytics Toolkit comes preconfigured to build AI sensor models directly for a variety of third-party embedded sensor evaluation kits such as those from Nordic Semiconductor, STMicro, and Quicklogic. Such kits are a great way to explore new application concepts and build early prototypes. Inevitably though, such kitted proof-of-concepts give way to need to customize sensors and/or substitute standard eval boards with custom board designs tailored to a specific end-use application.  This document describes the protocols, supporting documentation, and reference code needed for SensiML users to adapt their customized board firmware and SensiML software configuration to properly interface and work in a manner consistent with the supported evaluation kits"

The Role for Sensors in Intelligent IoT Networks

by Chris Rogers, June 28, 2018

Full Article

"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."

Smart Sensors Fulfilling The Promise Of The IoT

by Marcellino Gemelli, October 13, 2017

Full Article

"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."

PODCASTS

Podcast: Electronic Engineering Journal

Hearing is Believing: Artificial Intelligence, Cough Patterns, and Stemming the Tide of COVID-19

by Amelia Dalton, June 12, 2020


The June 12th installment of EE Journal's extremely popular Fish Fry podcast is all about noisy neurons, cough signature identification, and how AI can help us stem the tide of COVID-19... Chris Rogers (CEO – SensiML) joins us to chat about a new health monitoring solution to help fight the COVID-19 pandemic. We discuss the role of artificial intelligence in this solution and how this new platform may help detect symptoms earlier than before with identification of cough sounds.


Podcast: Embedded Computing Design

Special COVID-19 Edition of Embedded Executives: Chris Rogers, CEO, SensiML

by Rich Nass, May 28, 2020


Many pundits say that the key to neutralizing the COVID-19 virus is testing, lots of testing. That’s easier said than done, unless you believe what the technology experts at SensiML are saying. They claim that with enough data in hand, they can tell you whether you have the infection simply by coughing into a microphone. And the test should produce results that are about 90% accurate. Wow! To be clear, this does not meet the requirements of a clinical tool, but it’s a fantastic start.


VIDEOS

Webinar: Endpoint AI Without Writing Code

SensiML Overview

Industrial Machinery Monitoring Demo

Industrial Wearable Demo

Process Overview

Explainer Video

Educational Wearable Demo

Quick Start Video Tutorials

CASE STUDIES / TESTIMONIALS

Mando-Hella Electronics Rapidly Develops Smart Sensor Applications for New Markets with SensiML Analytics Toolkit

Full Article

"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."