Autoencoder Neural Engine Compressing Spectrograms for IoT Bandwidth Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
End node devices, such as battery-powered IoT devices, face significant challenges in performing meaningful computations on sensed data like speech or images due to limited processing and power resources, leading to prohibitive bandwidth and power consumption when transmitting raw data to the cloud for processing.
Innovation Solution
Incorporating a sensor, digitizer, signal processor, neural engine with an autoencoder, and wireless circuit to compress and transmit spectrograms, allowing for local minimal processing and efficient data transmission, with the neural engine using pre-trained models and updating coefficients based on remote feedback for improved processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If end nodes transmit raw data to the cloud, then processing accuracy is improved, but bandwidth consumption increases
Solution Approach 1:
The patent extracts only the essential features from raw data by using autoencoders to generate compressed representations. Instead of transmitting complete raw data, the system extracts and transmits only the most important information needed for accurate cloud processing, thereby reducing bandwidth consumption while maintaining processing accuracy.
Solution Approach 2:
The patent changes the parameter of data representation from raw uncompressed format to compressed latent space representation. By transforming data through neural network encoders, the system represents the same information using fewer parameters, reducing the quantity of data transmitted over bandwidth-constrained channels.
2Measurement precision
If end nodes transmit raw data to the cloud, then processing accuracy is improved, but power consumption increases
Solution Approach 1:
The patent extracts only the essential features from raw data before transmission. By using autoencoders to generate compressed representations, the system reduces the amount of data that needs to be transmitted and processed, thereby reducing the energy consumption of both the end node and the cloud processing system while maintaining processing accuracy.
Solution Approach 2:
The patent changes the data representation parameters from raw format to compressed format, reducing the computational burden and energy consumption associated with data transmission and processing. This parameter transformation enables accurate processing with significantly reduced power consumption.
3Measurement precision
If end nodes perform meaningful computations locally, then processing accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the processing architecture into two parts: a lightweight encoder at the end node that performs minimal local computation, and a more sophisticated decoder at the cloud. This segmentation allows the end node to maintain simplicity while still enabling accurate processing through the distributed architecture.
Solution Approach 2:
The patent introduces an autoencoder-based compression system as an intermediary between the end node and cloud processing. This intermediary transforms raw data into compressed representations, reducing the computational burden on the end node while preserving the information needed for accurate cloud-based processing.
Data Source
AI summary
In one embodiment, an apparatus includes: a sensor to sense real world information; a digitizer coupled to the sensor to digitize the real world information into digitized information; a signal processor coupled to the digitizer to process the digitized information into a spectrogram; a neural engine coupled to the signal processor, the neural engine comprising an autoencoder to compress the spectrogram into a compressed spectrogram; and a wireless circuit coupled to the neural engine to send the compressed spectrogram to a remote destination, to enable the remote destination to process the compressed spectrogram.