Embedded Hardware

SensiML at Embedded World 2024

April 15, 2024

Embedded World 2024 is a wrap. If you missed us in Nuremberg, read this summary of what SensiML showed at the show and a preview of upcoming events where you can find us.

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SensiML @ Silicon Labs Works With 2022

Accelerating AI/ML for the IoT
September 16, 2022

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.

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SensiML and Infineon Team Up to Add AI for PSoC™ 6 MCUs

Our SensiML Analytics Toolkit can now work together with the Infineon ModusToolbox™
February 4, 2022

We just announced that SensiML has teamed up with Infineon Technologies AG to deliver a complete AI/ML solution for the Infineon PSoC™ 6 family of microcontrollers (MCUs) and XENSIV™ sensors.  This is great news for Infineon customers as they can now use SensiML’s tools to easily create intelligent IoT endpoints using the ultra-low power and

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SensiML & Microchip Technology Solution

Ultra-Compact ML for IoT Sensing Applications
June 28, 2021

SensiML has collaborated with Microchip Technology to deliver ultra-compact machine learning at the IoT edge combining SensiML Analytics Toolkit with the SAM-IoT WG evaluation kit, SAMD21 MCU, and MPLAB X IDE tool suite.

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SensiML’s New Open Source Initiative: AI Transparency and Flexibility Supporting IoT Sensor Products for the Real-World

AI Transparency and Flexibility Supporting IoT Sensor Products for the Real-World
May 10, 2021

Today SensiML took a leadership position in AI/ML tools for the IoT edge by announcing our new Open Source Initiative. SensiML Open Source Embedded SDK (coming later this summer) – The full library of SensiML segmenters, transforms, features, and classifiers as implemented by the AutoML and Python-based SensiML Analytics Toolkit Notebook will become available in open source format.

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SensiML Adds Arduino Nano 33 BLE Sense Support with PlatformIO

November 20, 2020

This month SensiML is pleased to announce SensiML Analytics Toolkit now provides native support for STMicroelectronics’ SensorTile.Box Development Kit for wireless IoT and wearable sensor applications. SensiML’s support for SensorTile.Box includes our most sophisticated MQTT-based device data collection firmware yet made available in open source for customers to extend and modify as necessary to suit their particular application needs.

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SensiML Adds Support for STMicroelectronics’ SensorTile.Box

August 21, 2020

This month SensiML is pleased to announce SensiML Analytics Toolkit now provides native support for STMicroelectronics’ SensorTile.Box Development Kit for wireless IoT and wearable sensor applications. SensiML’s support for SensorTile.Box includes our most sophisticated MQTT-based device data collection firmware yet made available in open source for customers to extend and modify as necessary to suit their particular application needs.

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SensiML Adds Support for QuickLogic’s QuickFeather IoT Dev Kit

August 14, 2020

This week SensiML added support for the Quicklogic QuickFeather Development Kit. Noteworthy for its inclusion of Arm Cortex-M4 MCU, FPGA, and an array of sensors in a fully open-source HDK using the popular Adafruit Feather form factor, the QuickFeather makes a great IoT development platform for developers of consumer, wearable, and industrial products.

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SensiML’s Response to the Coronavirus Pandemic

May 11, 2020

SensiML announces two COVID-19 initiatives:
1) SensiML Toolkit Starter Edition is now available for a limited time at $99 for a 90-day license.
2) SensiML to open-source a COVID-19 AI dataset for rapid virus screening from cough sounds. Learn how you can contribute to this project yourself.

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The Case for AI at the Edge

February 21, 2020

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

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