Available technologies

Neural Network Wireless IoT Device

Reference number: 8387

8387-neuralnetpicNeural Network Wireless IoT Device
for highly energy efficient on-chip data processing

Developed by Thomas Heinis' group 
in Imperial College London's Department of Computer Science.

Overview

Wireless IoT sensors are becoming increasingly widespread in both wearables and environmental applications. With an increase in the data being captured, edge-processing is becoming more commonplace where data is wholly or partially pre-processed, before being sent to the cloud. The problem is that wireless IoT sensors are constrained in terms of power, memory and bandwidth. Computer Science researchers from Imperial College London have devised an efficient IoT sensor device, based on neural network training and very energy-efficient on-chip classification. This technology supports any number of IoT sensors of any type (e.g. pressure, temperature etc).
Initial testing suggests the new implementation saves 3x as much energy vs standard implementations. This IoT sensor device will enable better and more efficient on-chip machine-learning classification of data for a variety of application areas. We are seeking licensing partners interested in improving their products using this patented technology.

Benefits Applications
  • 3x energy saved vs standard implementations
  • Uses only 40% RAM to achieve the same classification accuracy
  • 22% better classification accuracy (with same memory footprint)
  • Biometrics (e.g. smart watches, glasses, fitness trackers, healthcare wearables, headsets, smart clothing, brain-computer interfaces).
  • Environmental sensors (collecting environmental data such as temperature and pressure).

Download our technology datasheet for this neural network energy efficient IoT chip

Download datasheet

contact

Rebeca Santamaria-Fernandez

Director of Industry Partnerships and Commercialisation, Engineering

r.santamaria-fernandez@imperial.ac.uk

+44 (0)20 7594 8599

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