Neural Network Wireless IoT Device
for highly energy efficient on-chip data processing
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.
Download our technology datasheet for this neural network energy efficient IoT chip
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