We are in a climate change crisis. Severe weather events, flooding, wildfires, air pollution, species extinction and so much more are impacting our entire ecosystem. It is up to us to spearhead technological innovation to mitigate these disasters.
Heterogeneous, ultra-low-power processors along with AI at the extreme edge are exciting technologies promising new insight at the scale and economics needed to address this massive problem. The challenge is huge but so is the possibility for applied smart IoT ingenuity. So, show the world what YOU can build that can make a difference!
100% Open Source Hardware Dev Kit and Software Tools based on QuickLogic’s EOS™ S3 Low Power, multi-core Arm Cortex® M4 MCU + eFPGA.More Info
MCU + eFPGA SoC Open Source Software ToolsMore Info
An end-to-end AI software suite designed for rapid and easy creation of smart IoT sensor algorithms on the smallest, low-power processors.More Info
A repository of sample datasets, SensiML models, and resulting IoT firmware for a variety of smart sensor applications.More Info
The QuickFeather Development Kit is a small form factor system ideal for enabling the next generation of low-power Machine Learning (ML) capable IoT devices. Unlike other development kits which are based on proprietary hardware and software tools, QuickFeather is based on open source hardware, compatible with the Adafruit Feather form factor, and is built around 100% open source software (including the Symbiflow FPGA Tools).
The QuickFeather is powered by QuickLogic’s EOS™ S3, the first FPGA-enabled Arm Cortex®-M4F MCU to be fully supported with Zephyr RTOS. Other functionality includes:
Order the QuickFeather Dev Kit and download the SensiML Community Edition.
Get started learning how SensiML Analytics Toolkit is used to build autonomous sensor algorithms from labeled training data by downloading the Free Trial version of the SensiML software here.
QuickFeather runs QORC (QuickLogic Open Reconfigurable Computing) software development platform, built on a strong foundation of comprehensive, open source tools
Start by visiting the QORC-SDK Github page, which has all the information needed to download and install the FreeRTOS SDK as well as few examples to get you started. Additional starter examples are also available on the QORC-SDK getting started page.
That takes care of the MCU side. Now you can learn more about how to program the eFPGA inside the EOS S3 at the QuickLogic Toolchain page.
Lastly, you can do a pure software emulation of your Proof of Concept by running your software on Renode, an open source tool developed by our partner, Antmicro.
Additional information for QORC
Programming a QuickFeather Dev Kit using TinyFPGA video
Running “Hello World” on a QuickFeather Dev Kit video
An end-to-end AI software suite designed for rapid and easy creation of smart IoT sensor algorithms on the smallest, low-power processors. SensiML automates the complex data science required to derive desired insights from time-series sensors allowing developers to focus on the application logic and collection of training data versus complicated math and generation of efficient low-power machine learning code.
After your application has been submitted, you will receive an email containing instructions for how to download the SensiML software along with a link for setting up your SensiML cloud account. Anyone who buys or is awarded hardware will get access to the SensiML software. For those who would like to starting learning how SensiML software works right away, a Free Trial version of the software is available from the SensiML website that allows you to explore the capabilities and features of the toolkit using example application datasets.
Account setup provides access to the SensiML cloud service for managing your AI sensor project(s), labeled sensor datasets, and AI code generation (note the Free Trial version is limited to exploration of provided sample datasets only). Once set up, you will be directed to a download page to acquire the latest versions of SensiML applications Data Capture Lab and TestApp. Data Capture Lab, which allows for easy but powerful data acquisition and labeling of IoT sensor data, requires a Windows 10 based PC. TestApp, a firmware test tool used to validate the functioning of your SensiML sensor algorithms on actual hardware, is available in both Windows 10 PC and Android mobile versions.
Following setup and install, you can get started learning the software right away by following our Quick Start Guide.
A repository of sample datasets, SensiML models, and resulting IoT firmware for a variety of sensor applications to help provide examples of what is possible using IoT edge AI models.
Visit the SensiML Data Depot repository and review available application examples, documentation, and sample datasets. Each application includes summary information on the hardware and sensor used, number of sample captures, and sector.
After selecting an application of interest by clicking “View Dataset”, documentation and download links are provided in the details page for that application. Once downloaded, datasets can then be imported into SensiML Data Capture Lab and used to explore or tune an existing application or as a starting point for creating something new.
Any intelligent IoT device application where edge AI processing of physical sensors can improve awareness, change behavior, or optimize processes to impact climate change for the better. To spark your creative thinking, below are example projects in each of the major contributing sectors of greenhouse gas emissions.
Help the electrical grid to accommodate more renewable energy by enabling power modes or charging modes that respond to appropriate timings or signals from electricity providers.
Smart power-downs, unusual power use alerts, vampire power drain reporting
AI process control sensors that optimize inputs to reduce energy consumption or change timing of consumption of equipment like pumps, extruders, kilns and dryers.
Forest management sensor nets that use seismic, audio, air quality, and temperature sensor processing to detect illegal cutting and provide wildfire early warning.
Micro-sensing and control of water requirements and watering progress; sensing of malfunctioning water systems.
Ubiquitous smart road sensors that use vibration and audio to track traffic patterns and manage congestion, thus reducing emissions from idling vehicles.
Smart wood/pellet/cook stove combustion mechanisms that use fuel type inputs and combustion sensing to reduce particulate matter and gas emissions.
Ubiquitous sensors that are used for tracking methane release across energy production and pipeline infrastructure for pinpointing valve breakdowns and other maintenance needs
Widespread CO2 monitoring sensors to help optimize smart energy grid operation (e.g. enabling grid operators to manage electricity generation resources as not all gas turbines are equally efficient.)
Erin Craig brings more than 30 years’ experience in sustainability, climate change, and technology service delivery across Fortune 500 companies including Apple, Salesforce and Sun Microsystems.
Since 2007, Erin has focused her efforts on climate change with climate solutions company 3Degrees. Today, she leads the company's Customer Solutions and Innovation team, responsible for bringing new product and service models to market; she’s also a Board director. Earlier in her tenure she engaged with emitters to develop on-the-ground reduction projects, and launched a consulting practice that now provides climate strategy and implementations for many of the world’s largest corporations.
She co-chairs the Innovation Committee of the Renewable Energy Buyers' Alliance, and was named as one of “The 100 most creative people in business” by FastCompany in 2019.
Erin has an MS from MIT in Technology and Policy, and a BS in Geophysics from Stanford University.
Tim Callahan works at Google helping create open source tools for FPGAs, and more generally helping make FPGA development accessible, fun, and rewarding. Before Google, he worked in various roles developing compilers and synthesis tools for FPGAs and other non-von-Neumann platforms.
He holds a BSEE degree from the University of Minnesota, a Diploma from Cambridge University, and a PhD in Computer Science from Berkeley.
Timothy Saxe (Ph.D) has served as our Senior Vice President and Chief Technology Officer since November 2008. In August 2016, he expanded the role to include Senior Vice President of Engineering. Mr. Saxe has been with QuickLogic since May 2001 and during the last 15 years has held a variety of executive leadership positions including Vice President of Engineering and Vice President of Software Engineering. Dr. Saxe was Vice President of FLASH Engineering at Actel Corporation, a semiconductor manufacturing company. Dr. Saxe joined GateField Corporation, a design verification tools and services company formerly known as Zycad, in June 1983 and was a founder of their semiconductor manufacturing division in 1993. Dr. Saxe became GateField's Chief Executive Officer in February 1999 and served in that capacity until GateField was acquired by Actel in November 2000. Mr. Saxe holds a B.S.E.E. degree from North Carolina State University, and an M.S.E.E. degree and a Ph.D. in electrical engineering from Stanford University.
Chris Knorowski is the co-founder and CTO at SensiML where he builds tools to make it easier for developers an engineer’s create smart sensor algorithms capable of running at the extreme edge. Prior to SensiML he worked as software engineer and data scientist at Intel and Dupont Pioneer. He holds a Ph.D in computational physics from Iowa State and a B.S. in Physics from Virginia Tech.