Switch Layer Neural Network Routing for Computational Capacity
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Solution Overview
Problem
Existing neural networks face challenges in increasing their computational capacity without a corresponding increase in computational resources, such as FLOPS, which limits their performance on machine learning tasks.
Innovation Solution
The implementation of a neural network system that includes multiple layers, at least one of which is a switch neural network layer. This switch layer increases the number of parameters without increasing the per-input computational cost by always selecting a single expert from a set of expert neural networks for any given input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of parameters in a neural network is increased to improve computational capacity, then the performance on machine learning tasks is improved, but the computational resources (FLOPS) required per input increases
Solution Approach 1:
The neural network is segmented into multiple expert networks, each handling a subset of computational tasks. The switch layer divides the input stream and routes portions to different experts, allowing the system to achieve high computational capacity through parallel specialization rather than through increasing the size of a single monolithic network, thus avoiding proportional increases in FLOPS.
Solution Approach 2:
The switch layer dynamically selects which expert networks to activate based on the specific input characteristics. This dynamic routing mechanism allows the system to adaptively allocate computational resources, activating only the necessary experts for each input rather than processing through all parameters, thereby maintaining constant FLOPS while achieving high adaptability.
2Adaptability or versatility
If multiple expert neural networks are used to increase parameter count, then the model capacity increases, but the router computation and communication costs increase
Solution Approach 1:
The switch layer processes only a subset of inputs through each expert network rather than routing all inputs to all experts. By selectively activating only the necessary experts for each input token, the system achieves high model capacity while keeping the actual computational workload and communication costs bounded and manageable.
Solution Approach 2:
The patent extracts and separates the routing function into a distinct switch layer that operates independently from the expert networks. This extraction allows the router to make lightweight decisions about which experts to activate without requiring complex coordination between all components, reducing overall system complexity while maintaining high model capacity.
3Ease of manufacture
If the batch size of each expert is reduced to simplify routing implementation, then the communication costs decrease, but the expert capacity is reduced
Solution Approach 1:
Multiple expert networks with reduced individual batch sizes are merged at the switch layer to achieve the equivalent of a single large-capacity expert. The switch layer combines the outputs of multiple smaller experts, effectively aggregating their capacities while keeping each expert's implementation simple and communication costs low.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network including one or more switch layers.