Attention-Based 3D Object Detection With ReLU Normalization for Edge Devices
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Solution Overview
Problem
Existing attention-based object detection methods are computationally intensive and memory-consuming, making them difficult to run on resource-constrained devices such as edge devices, particularly due to the use of Softmax layers and cross-attention calculations.
Innovation Solution
Implementing an attention engine with rectified linear units (ReLUs) and normalization layers to replace Softmax functions, combined with a downsampling layer to reduce feature size, allowing efficient attention-based object detection on various devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Softmax layers and cross-attention calculations are used in attention-based object detection methods, then detection accuracy is improved, but computational intensity and memory consumption increase significantly
Solution Approach 1:
The patent changes the mathematical parameters of the attention mechanism by replacing Softmax functions with Rectified Linear Unit (ReLU) activation functions and using normalized attention scores. This parameter change maintains the attention mechanism's ability to weigh important regions while significantly reducing computational complexity and memory requirements, thus resolving the contradiction between detection accuracy and computational intensity.
2Measurement precision
If Softmax layers and cross-attention calculations are used in attention-based object detection methods, then detection accuracy is improved, but memory consumption increases significantly
Solution Approach 1:
The patent modifies the memory requirements by changing the mathematical operations from Softmax to ReLU and using normalized attention scores. This parameter change reduces the amount of data that needs to be stored and processed during attention calculations, thereby reducing memory consumption while maintaining detection accuracy.
3Measurement precision
If existing attention-based object detection methods are used, then detection performance is improved, but device complexity increases making it difficult to run on resource-constrained devices
Solution Approach 1:
The patent simplifies the device complexity by changing the mathematical operations from computationally intensive Softmax and cross-attention to more efficient ReLU and normalized attention. This parameter change reduces the computational resources required, enabling the system to run on resource-constrained devices while maintaining detection performance.
Data Source
AI summary
Systems and techniques are described herein for attention-based object detection. For example, a computing device can process a key via a first rectified linear unit of an attention engine of a machine learning model to generate a first output. The computing device can process the first output via a first normalization layer of the attention engine to generate a second output. The computing device can compute a dot product based on the second output and a value to generate a third output. The computing device can process a query via a second rectified linear unit of the attention engine to generate a fourth output. The computing device can process the fourth output via a second normalization layer of the attention engine to generate a fifth output. The computing device can compute a dot product based on the third output and the fifth output to generate a sixth output.