LSTM Cell Architecture for Sensor Fusion Gradient Management
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Solution Overview
Problem
Recurrent neural networks, particularly LSTM cells, face challenges in sensor fusion due to the vanishing gradient problem, which hinders effective learning and information flow over arbitrary time intervals.
Innovation Solution
The proposed solution involves an LSTM cell design for sensor fusion, incorporating multiple forget gates, input gates, and output gates. These gates receive sensor encodings and shared hidden states, employing sigmoid, hyperbolic tangent, and element-wise product functions to generate outputs. The cell state and hidden state are updated through element-wise operations, allowing for parallel architectures to enhance fusion efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional LSTM cells are used for sensor fusion, then the architecture can process sequential data, but the vanishing gradient problem prevents effective learning over arbitrary time intervals
Solution Approach 1:
The patent divides the traditional single LSTM cell into multiple parallel LSTM cells, each processing different sensor encodings. This segmentation allows independent gradient flow paths for each sensor type, preventing the vanishing gradient problem that occurs when gradients must propagate through concatenated sensor data in traditional architectures.
Solution Approach 2:
The patent merges the outputs of multiple parallel LSTM cells through element-wise addition to create a unified hidden state. This combining approach allows gradients to flow independently through each parallel path while still achieving sensor fusion at the output level, maintaining reliable learning capability across arbitrary time intervals.
2Adaptability or versatility
If multiple sensor encodings are processed through a single LSTM cell, then sensor fusion can be achieved, but the gradient shrinks during back propagation
Solution Approach 1:
The patent segments the processing of multiple sensor encodings by routing each sensor encoding through a separate parallel LSTM cell. This segmentation creates independent gradient computation paths, preventing the gradient shrinkage that occurs when multiple sensor encodings are concatenated and processed through a single LSTM cell.
Solution Approach 2:
The patent transitions from processing sensor encodings in a single sequential path to processing them in parallel dimensions. By organizing LSTM cells in parallel architecture where each cell handles a specific sensor encoding, the system maintains gradient magnitude while achieving sensor fusion through the dimensional expansion of parallel processing paths.
3Loss of information
If parallel LSTM architectures are used for sensor fusion, then information flow improves, but the device complexity increases
Solution Approach 1:
The patent applies universality by using identical LSTM cell structures across all parallel paths. Each parallel LSTM cell uses the same architecture, weights, and processing mechanisms, allowing the system to handle multiple sensor encodings with a universal processing template. This reduces the practical complexity increase despite the parallel architecture.
Solution Approach 2:
The patent changes the parameter organization from shared weights in a single cell to independent weights in parallel cells. By allowing each parallel LSTM cell to have its own learned parameters while maintaining the same architectural structure, the system optimizes information flow for each sensor type while keeping the overall complexity manageable through parameter specialization.
Data Source
AI summary
According to one aspect, a long short-term memory (LSTM) cell for sensor fusion may include a first architecture receiving a first sensor encoding, a first shared cell state, and a first shared hidden state and generating a first set of outputs based on the first sensor encoding, the first shared cell state, and the first shared hidden state, a second architecture receiving a second sensor encoding, the first shared cell state, and the first shared hidden state and generating a second set of outputs based on the second sensor encoding, the first shared cell state, and the first shared hidden state, a hidden state gate generating a second shared hidden state based on the first set of outputs and the second set of outputs, and a cell state gate generating a second shared cell state based on the first set of outputs and the second set of outputs.