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." 

SUPPORTED PLATFORMS

QuickLogic - QuickAI Accelerated AI Platforms

QuickLogic's QuickAI™ and EOS™ S3AI  platforms are both supported within SensiML Analytics Studio.  Unique ultra-low power AI platforms with heterogeneous processing cores, the QuickLogic family of endpoint AI platforms allow SensiML algorithms to be implemented with hardware optimization for FPGA, DSP, and neuromorphic memory.

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. Full details can be found at the Quicklogic QuickAI product site.

ST - STM32 & SensorTile Development Kit

The SensorTile is a tiny, square-shaped IoT module that packs powerful processing capabilities leveraging an 80 MHz STM32L476JGY microcontroller and Bluetooth low energy connectivity based on BlueNRG-MS network processor as well as a wide spectrum of motion and environmental MEMS sensors, including a digital microphone.

Nordic Semiconductor - nRF52 & Nordic Thingy IoT Sensor Kit

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

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

The Intel® Atom™ processor line is 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

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. 

Raspberry Pi 3/3B

is supported within SensiML Analytics Studio

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.

SUPPORTED FEATURES

Fully Supported Embedded Sensor Endpoint Platforms

  QuickLogic Merced
(EOS S3B-AI)
QuickLogic Merced
(EOS S3B-AI)
+ Mayhew
QuickLogic Chilkat
(EOS S3B-AI)
ST Sensor Tile
(STM32)
Nordic Thingy
(nRF52)
Accelerometer
Sensor Component ST LSM6DSL ST LSM6DSL Bosch BMI160 ST LSM6DSM TDK MPU-9250
Supported Output Data Rates (Hz) 26, 52, 104, 208, 416, 1660 26, 52, 104, 208, 416, 1660 25, 50, 100 26, 52, 104, 208, 416 5, 10, 25, 50, 100, 200
Support Range (Gs) ± 2 ± 2 ± 2 ± 2, ± 4, ± 8, ± 16  ± 2
Gyroscope
Sensor Component ST LSM6DSL ST LSM6DSL Bosch BMI160 ST LSM6DSM TDK MPU-9250
Supported Output Data Rates (Hz) 26, 52, 104, 208, 416, 1660 26, 52, 104, 208, 416, 1660 25, 50, 100 26, 52, 104, 208, 416 5, 10, 25, 50, 100, 200
Support Range (DPS) ± 2000 ± 2000 ± 2000 ± 250, ± 500,
± 1000, ± 2000
± 2000
External Analog Input (ADC)        
Sensor Component   LTC-1859      
Supported Output Data Rates (Hz)   1 ch x 100kHz,
1-4 ch x 16 kHz
     
Support Range (V)   ± 5, ± 10      
Supported Input Modes   Single Ended, Differential      
Microphone
Sensor Component Knowles
SPH0641LM4H
Knowles
SPH0641LM4H
Knowles
SPH0641LM4H
ST
MP34DT04/DT05
ST
MP34DB02
Supported Output Data Rates (Hz) 16 kHz 16 kHz 16 kHz 16 kHz 8 kHz
(lossy comp)
Multi-Sensor Recognition
IMU + Audio  
IMU + ADC        
Communication BLE BLE BLE BLE BLE
Component nRF51822 nRF51822 nRF51822 BlueNRG-MS nRF52832
Raw Sensor Data Storage SDCard SDCard   SDCard  
Capacity Up to 32GB Up to 32GB   Up to 32GB  
Voice Recognition    
Analytics Studio Supported Platforms*
Raspberry Pi 3
Intel Atom/Core Processors

* - As generic embedded platforms, the following can be supported for algorithm code generation provided customer can supply needed sensor libraries and perform sensor integration effort.  Native support in Data Capture Lab and TestApp requires use of SensiML API to write conformant interface libraries.  

USEFUL ARTICLES AND LINKS

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."

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."

VIDEOS

Webinar: Endpoint AI Without Writing Code

SensiML Overview

Industrial Machinery Monitoring Demo

Industrial Wearable Demo

Process Overview

Quick Start Video Tutorials

Explainer Video

Educational Wearable Demo

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."