Heatmap Regression Network Topology Without Upscaling Bottlenecks
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Solution Overview
Problem
Conventional neural networks for heatmap regression require significant computational resources, memory, and are slow due to complex topologies involving downscaling and upscaling processes, making them inefficient for systems with restricted resources.
Innovation Solution
A neural network topology that omits the resolution recovery stage by using depthwise and pointwise convolution bottlenecks, with pooling layers limiting spatial resolution to the output layer, and employing quantization to 8 bits or lower, optimizing performance for embedded systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural networks use downscaling and subsequent upscaling topology for heatmap regression, then resolution recovery is achieved, but memory resources, power consumption, and processing time increase significantly
Solution Approach 1:
The patent removes the upscaling stage from the conventional downscaling-upscaling topology. By extracting only the necessary downscaling operation and eliminating the redundant upscaling step, the network achieves heatmap regression with reduced computational complexity while maintaining acceptable resolution accuracy for embedded systems.
Solution Approach 2:
Instead of following the conventional approach of downscaling then upscaling to recover resolution, the patent inverts the logic by working directly at the reduced resolution space throughout the network. This inversion eliminates the need for resolution recovery and directly produces heatmaps at the target resolution, significantly reducing memory and computational requirements.
2Measurement precision
If conventional neural networks perform multiple upscaling operations for resolution recovery, then output resolution is improved, but processing speed decreases and power consumption increases
Solution Approach 1:
The patent extracts and removes the upscaling operations from the network topology. By eliminating these computationally intensive operations, the network processes images faster with lower power consumption while still achieving the required output resolution through direct downsampling to the target dimensions.
3Measurement precision
If conventional neural networks use complex topologies with downscaling and upscaling stages, then resolution recovery is achieved, but memory resources are consumed excessively
Solution Approach 1:
The patent removes the upscaling stage that requires significant memory resources for storing intermediate high-resolution feature maps. By working directly at reduced resolution throughout the network, memory consumption is dramatically reduced while still producing heatmaps at the desired output resolution.
Solution Approach 2:
The patent inverts the conventional resolution handling approach by maintaining reduced resolution throughout the entire network rather than recovering to high resolution. This inversion eliminates the memory burden of storing and processing large high-resolution feature maps while achieving the required output quality.
Data Source
AI summary
Methods, systems, and apparatuses for heatmap regression for neural networks are provided, including for systems with restricted computational resources, such as embedded systems. An exemplary method may include utilizing a trained neural network having a plurality of pooling layers, a plurality of convolutional layers, and an output layer to generate a heatmap. The plurality of pooling layers may reduce the spatial resolution of an input image, though the resolution may not be reduced below the spatial resolution of the output layer. The plurality of convolutional layers may utilize a plurality of depthwise convolutions and pointwise convolutions that will be stacked to provide a plurality of bottlenecks.