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QuickAI™

With the advent of Industry 4.0 and the proliferation of IoT, there is a need to make smarter devices that process the sensor data AND make decisions LOCALLY. Traditional cloud-based approaches to designing AI systems make it difficult to migrate AI to endpoint devices due to aggressive battery life, processing capability and environmental constraints. These limitations and requirements call for a different type of artificial intelligence at the endpoint device – one that is zero latency, low power, low cost and easy to train. Security is a non-factor as sensor data never leaves the endpoint device, like in traditional cloud based AI systems.

Through QuickLogic’s acquisition of SensiML, the QuickAI HW/SW platform includes the SensiML Analytics Toolkit that enables OEMs to develop AI software for a broad array of resource-constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications including wearables, mobile, predictive maintenance, factory automation, structural health monitoring and smart harvesting equipment.

Sensor processing

eFPGA for feature extraction

Neurons for AI computing

Data analytics SW for data training and model/classifier building

Scaling across bigger AI systems with thousands of endpoint devices

No need for in-house expertise of data analytics, DSP processing, app coding

Delivers an AI module solution that can be deployed at the endpoint devices with different connectivity, sensor

Challenge

  • Companies developing endpoint devices often lack the resources to work effectively with the complexities of AI development: data collection and training, feature extraction, and classifier and AI model generation.
  • Industrial and manufacturing OEMs, who are working to address the Industrial 4.0 initiative, lack the data science and firmware engineering resources needed to develop AI models and implement them in a low power SoC platform.
  • The diversity and uniqueness of endpoint use cases drive the need to develop application- specific algorithms and models, thus requiring large SW teams to address numerous product lines, and even derivative products, that exhibit different sensor characteristics.
  • Once endpoint devices are deployed, manufacturers must have a plan to manage the distributed endpoints, leverage the information they collect, and often update AI models remotely.

Solution

QuickAI: An integrated HW/SW solution that enables AI for IoT devices

  • Low power sensor processing
  • Data capture lab for data collection, labeling and training
  • Data analytics SW for AI model and classifier generation
  • Neuron development for AI computing
  • Automatic firmware development for the S3AI multi-core SoC platform
  • eFPGA low power implementation for HW acceleration and feature extraction

SensiML Analytics Toolkit for AI

  • An end-to-end software suite that provides OEMs with a straightforward process for developing pattern matching sensor algorithms. It uses machine learning technology and is optimized for ultra-low power consumption.
  • Enables OEMs to quickly and easily leverage the power of local AI in edge, endpoint and wearable designs without the need for significant data science or firmware engineering resources.
  • Automatically optimizes AI models to minimize power consumption in targeted SoCs, and is designed specifically to leverage the inherent benefits of heterogeneous multi-core SoC architectures and eFPGA technology.
  • For more info: www.sensiml.com

QuickAI End-to-End Platform Solution for Time Series

 
QuickAI End-to-End Time Series Platform Solution
QuickAI Hardware Product Offering
  • QuickAI Merced HDK
  • EOS S3AI
QuickAI Documentation
  • EOS S3AI Datasheet
  • EOS S3AI SDK Guide
  • EOS S3AI Merced 1.1 HDK User’s Guide
  • SensiML QuickStart Guide
QuickAI Software Product Offering & Support Models
  • EOS S3AI SDK
QuickAI Collateral
  • QuickAI Brochure
  • EOS S3AI Product Brief
  • Merced HDK Brochure
  • SensiML Toolkit Brochure

Industrial IoT

Industrial Predictive Maintenance

Industrial Predictive Maintenance
  • Unique model doesn’t scale across similar motor differences in mounting or loading
  • Endpoint AI decreases system bandwidth, latency, power
Algorithm Development: SensiML Toolkit for Time Series
  • Data collection, segmenting, labeling
  • Sensor input: motion, audio, pressure, temp/humidity, other time series data
  • Feature extraction
  • Model building
FPGA Features
  • Sensor Data Creation → Feature Extraction → Feature Vector
  • Hardware accelerator (FFT & MFCC)
FFE Enabled Features
  • Event trigger (segmentation)
  • Feature extraction for simpler features
  • Ultra-low power AON function

SHM (Structural Health Monitoring)

Structural Health Monitoring
  • Damage detection
  • Structural Integrity reporting
Algorithm Development: SensiML Toolkit for Time Series
  • Data collection, segmenting, labeling Sensor input: motion, audio
  • Feature extraction
  • Model building
FPGA Features
  • Sensor Data Creation → Feature Extraction → Feature Vector
  • Hardware accelerator (FFT)
FFE Enabled Features
  • Event trigger (segmentation)
  • Feature extraction for simpler features
  • Vibration (high precision accel) analysis at 200Hz ODR
We provide a complete evaluation platform
Learn More About Merced HDK

Wearable System Architecture

S3AI as a Sensor Hub

Smart Watch Application
Voice + AI Algorithms
Target Applications
  • Wearable, IoT
  • Elderly Care
  • Animal Tracker
Voice
  • Fixed Trigger and KWD
  • Low Power Sound Detection
SensiML AI Algo Generator
  • Activity Classifier (Walk, Run, Bike, Still, Transport)
  • Gestures: Bring-to-See, Double-Tap , Rotate
  • Context: Fall Detection
Other Value Proposition:
  • FPGA
  • FFE (uDSP) Programmability
  • Other Custom Algos on M4

Wearable System Architecture

S3AI as Host

IoT Application
Voice + AI Algorithms
Target Applications
  • Wearable, IoT
  • Elderly Care
  • Animal Tracker
Voice
  • Fixed Trigger and KWD
  • Low Power Sound Detection
SensiML AI Algo Generator
  • Activity Classifier (Walk, Run, Bike, Still, Transport)
  • Gestures: Bring-to-See, Double-Tap , Rotate
  • Context: Fall Detection
Other Value Proposition:
  • FPGA
  • FFE (uDSP) Programmability
  • Other Custom Algos on M4
S3AI evaluation platform

Downloads

QuickAI Platform Product Brief
Merced HDK Product Brief
...