EnvelopeNet Neural Network Architecture for Efficient Image Recognition
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Solution Overview
Problem
Existing neural network architectures for image recognition require extensive search over the entire space of network architectures to find the optimal configuration, which is computationally expensive and inefficient, especially when resources such as energy, memory, and bandwidth are constrained in smart object networks.
Innovation Solution
The construction of EnvelopeNets, which involves forming a neural network from superset cells called envelope cells, where filters with low variance metrics are pruned and new envelope cells are appended to the network, optimizing the architecture through iterative training and restructuring without exhaustive search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive search over entire space of network architectures is performed to find optimal configuration, then optimal network architecture is achieved, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-defining a set of candidate operations (convolution, pooling, normalization, activation) and their possible configurations before training begins. This allows the network to be constructed from predetermined building blocks rather than searching the entire architecture space during training, significantly reducing computational cost while still enabling optimal architecture discovery through iterative selection of the best pre-defined operations.
Solution Approach 2:
The patent segments the network architecture search problem into discrete, manageable operations (convolution, pooling, normalization, activation) that can be independently selected and combined. By dividing the architecture design into modular operational units, the system can efficiently evaluate and select optimal combinations without performing exhaustive search over all possible continuous architecture configurations.
2Measurement precision
If extensive architecture search is performed to optimize network configuration, then image recognition accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent reduces energy consumption by pre-defining candidate operations and their configurations before training. This preliminary preparation allows the system to avoid computationally expensive exhaustive architecture search during the energy-constrained training phase, while still achieving high accuracy through iterative selection from the pre-defined operation set.
Solution Approach 2:
The patent changes the search space parameters by restricting the architecture optimization to a predefined set of discrete operations rather than allowing continuous parameter optimization. This parameter restriction reduces the computational complexity and energy required for architecture search while maintaining the ability to achieve high recognition accuracy through selective combination of operations.
3Reliability
If memory and bandwidth resources are increased to support exhaustive architecture search, then optimal network configuration can be found, but resource constraints in smart object networks are violated
Solution Approach 1:
The patent resolves the resource constraint issue by performing preliminary preparation of candidate operations before deployment to resource-constrained smart object networks. The pre-defined operation set and their configurations are prepared in advance, allowing the network to be trained and optimized with minimal memory and bandwidth requirements during the actual training phase, thus complying with smart object network constraints.
Solution Approach 2:
The patent segments the architecture search into discrete operational units that can be independently evaluated and selected. This segmentation allows for efficient memory usage by only storing and processing the necessary parameters for selecting from pre-defined operations, rather than requiring substantial memory resources to handle exhaustive architecture search, making the approach suitable for smart object networks with limited resources.
Data Source
AI summary
In one embodiment, a device forms a neural network envelope cell that comprises a plurality of convolution-based filters in series or parallel. The device constructs a convolutional neural network by stacking copies of the envelope cell in series. The device trains, using a training dataset of images, the convolutional neural network to perform image classification by iteratively collecting variance metrics for each filter in each envelope cell, pruning filters with low variance metrics from the convolutional neural network, and appending a new copy of the envelope cell into the convolutional neural network.