QuickLogic - QuickAI Accelerated AI Platform

QuickAI™ will be natively supported within SensiML Analytics Studio upon general availability in early Q3.

Full Details

"QuickLogic’s QuickAI platform for endpoint artificial intelligence (AI) applications provides an all-inclusive low power solution and development environment to economically incorporate the benefits of AI in endpoint applications. It features technology, software and toolkits from General Vision, Nepes, SensiML and QuickLogic, all of which have formed a tightly-coupled ecosystem to solve the challenges associated with the implementation of AI for endpoint applications."

Nordic Semiconductor - nRF52 & Nordic Thingy IoT Sensor Kit

Nordic Thingy:52® is natively supported within SensiML Analytics Studio

Full Details

"The Nordic Thingy:52® is a compact, power-optimized, multi-sensor development kit. It is an easy-to-use development platform, designed to help you build IoT prototypes and demos, without the need to build hardware or write firmware."

Intel Atom E Processors

Intel® Atom™ (and other x86 compatible processors) are supported within SensiML Analytics Studio

Full Details

"Intel® Atom™ processor line built for embedded applications. Power mobile, portable, and small-scale devices of every kind. Choose from processor/chipset combinations and system-on-chip configurations that deliver excellent performance per watt, rich graphics, and I/O integration."

ARM Cortex-M Series Processors

ARM Cortex-M3 and higher processors are supported within SensiML Analytics Studio

Full Details

"The Arm Cortex-M processor family is a range of scalable, energy efficient and easy-to-use processors that meet the needs of tomorrow’s smart and connected embedded applications. The processors are supported by the world’s #1 embedded ecosystem and have already been shipped in tens of billions of devices. Cortex-M processors help developers deliver more features, in less time, at a lower cost, with versatile connectivity, comprehensive code reuse, standard security and state of the art energy efficiency."

Raspberry Pi 3/3B

is supported within SensiML Analytics Studio

Full Details

"The Raspberry Pi 3 Model B+ is the latest product in the Raspberry Pi 3 range, boasting a 64-bit quad core processor running at 1.4GHz, dual-band 2.4GHz and 5GHz wireless LAN, Bluetooth 4.2/BLE, faster Ethernet, and PoE capability via a separate PoE HAT. The dual-band wireless LAN comes with modular compliance certification, allowing the board to be designed into end products with significantly reduced wireless LAN compliance testing, improving both cost and time to market."


SensiML Toolkit Documentation

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." 

Smart Sensor Endpoints

Opportunities for ML Analytics at the Sensor Endpoint

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."

AI at the IoT Endpoint - Quicklogic Fosters Sensing Ecosystem

by Kevin Morris, May 16, 2018

Full Article

"Computation is entering an era of unprecedented heterogeneous distribution. The diverse demands of IoT applications require everything from heavy-iron, deep-learning data-crunching to ultra-low-latency snap recognition and judgment. Our IoT devices and systems must be simultaneously aware and responsive to their own local context and able to harness the power of massive compute resources for more global issues. A self-driving vehicle can’t afford to send gobs of raw sensor data upstream to the cloud and then wait for an answer on target identification to return before deciding whether to brake or swerve. It needs to decide immediately whether or not there’s a human in the crosswalk, but it can wait awhile before rendering an AI judgment on whether the pedestrian’s attire was fashionable....

Life would be easy if every engineering team included data scientists who could design the training regimens working hand in hand with hardware experts who could partition the problem between conventional software, programmable hardware, and specialized neural network configuration. But life is not easy. Most projects don’t have access to the wide range of skills and expertise required to optimally engineer an AI endpoint for their IoT design. To make that happen, we need an ecosystem with plug-and-play hardware, software, and AI components and IP that will allow an average engineering project to take advantage of endpoint AI. This month, QuickLogic and several partners are introducing just such an ecosystem.... The collaboration with specialized AI players like General Vision, Nepes, and SensiML creates a robust development platform that should eliminate much of the friction for design teams wanting to take advantage of AI technology at the IoT edge."

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."

Sensor Hardware

Bosch Sensortec - BMI160 Inertial Measurement Unit

Bosch Sensortec IMUs are natively supported within SensiML Analytics Studio

Full Details

"The BMI160 is a small, low power, low noise 16-bit inertial measurement unit designed for use in mobile applications like augmented reality or indoor navigation which require highly accurate, real-time sensor data."


Explainer Video

Industrial Wearable Demo

Software Overview Video


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."