Neuro-Decoder for Power-Efficient ML Image Processing
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Solution Overview
Problem
Traditional image and video decoding operations in machine learning pipelines are sequential and power-intensive, leading to significant power wastage due to the performance gap between decoding compute power and AI workload processing.
Innovation Solution
An agent, such as a neuro-decoder, is integrated into the machine learning pipeline to operate directly on compressed bitstreams, filtering out irrelevant images and only decompressing and analyzing relevant ones, thereby reducing unnecessary decoding and preprocessing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional sequential decoding operations are performed on all compressed media, then complete decoding is achieved, but power consumption increases significantly
Solution Approach 1:
The patent extracts and processes only the essential features from compressed media bitstreams using selective decoding and feature extraction techniques. Instead of fully decoding all media, the system extracts key visual features directly from compressed data, eliminating unnecessary decoding operations and reducing power consumption while maintaining analysis effectiveness.
Solution Approach 2:
The patent segments the media processing pipeline into distinct stages: compressed bitstream analysis, selective feature extraction, and AI model processing. This segmentation allows the system to process only relevant features from compressed data rather than fully decoding entire media files, thereby reducing overall power consumption while maintaining processing throughput.
2Reliability
If all compressed images are decoded and preprocessed, then no relevant data is missed, but computational overhead increases
Solution Approach 1:
The patent performs preliminary analysis on compressed bitstreams before full decoding by extracting key visual features directly from compressed data. This preliminary feature extraction identifies potentially relevant images without requiring complete decoding, reducing computational overhead while ensuring that relevant data is not missed through subsequent verification stages.
Solution Approach 2:
The patent introduces an intermediary feature extraction layer between compressed bitstream input and AI model processing. This intermediary stage extracts essential visual features from compressed data without full decoding, acting as a mediator that reduces computational complexity while preserving the information needed for accurate analysis.
3Speed
If decoding compute power is increased to match AI workload processing, then performance gap is reduced, but power wastage increases
Solution Approach 1:
The patent applies partial decoding action by processing only the necessary portions of compressed media data. Instead of fully decoding all images to match AI processing speed, the system performs selective feature extraction on compressed bitstreams, achieving sufficient processing speed for the AI workload while avoiding the power wastage associated with excessive full decoding operations.
Data Source
AI summary
Systems, apparatuses and methods may provide for technology that filters, during a training phase of a machine learning (ML) pipeline, first irrelevant images from a first compressed bitstream based on reinforcement learning feedback from the ML pipeline, wherein the first irrelevant images are filtered from the first compressed bitstream prior to the first compressed bitstream being transmitted to a decompression stage of the ML pipeline, identifies, during an inference phase of the ML pipeline, second irrelevant images in a second compressed bitstream, and filters, during the inference phase of the ML pipeline, the second irrelevant images from the second compressed bitstream prior to the second compressed bitstream being transmitted to the decompression stage of the ML pipeline.