Neural Network Finetuning With Hollowed Layers for On-Device Personalization
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Solution Overview
Problem
Existing methods for finetuning generative machine-learning models, such as diffusion models, are memory-intensive and computationally complex, making on-device personalization challenging due to the large number of parameters and memory demands, especially on devices with limited resources.
Innovation Solution
The approach involves finetuning a smaller 'hollowed' neural network, generated by removing non-essential layers from a full neural network, in a two-stage process, where a forward pass generates intermediate activations, and a backward pass updates parameters of the hollowed network without loading the full network, reducing memory and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full neural network is used for finetuning, then the quality of generated outputs is maintained, but memory usage and computational resources increase significantly
Solution Approach 1:
The patent extracts only the essential neural network layers needed for finetuning by removing non-essential layers from the full network, creating a hollowed neural network. This extraction approach maintains the core functionality and quality of generated outputs while significantly reducing the number of parameters and memory requirements, directly resolving the contradiction between maintaining output quality and reducing memory usage.
Solution Approach 2:
The patent segments the neural network into essential and non-essential layers, separating the core processing components that maintain output quality from the auxiliary layers that consume memory. By processing data through only the essential layers during finetuning, the system achieves efficient personalization with reduced computational resources while preserving the quality of generated outputs.
2Reliability
If a full neural network is used for finetuning, then the quality of generated outputs is maintained, but computational complexity increases
Solution Approach 1:
The patent extracts and removes non-essential neural network layers from the full network, creating a simplified hollowed network that maintains only the core computational functions needed for quality output generation. This extraction reduces the computational complexity of finetuning operations while preserving the essential intelligence required for high-quality generated outputs.
Solution Approach 2:
The patent applies partial action by processing data through only the essential layers of the neural network during finetuning, rather than through all layers. This partial processing approach reduces computational complexity and resource consumption while maintaining sufficient accuracy for quality output generation, avoiding the excessive computational burden of processing through the complete network.
3Adaptability or versatility
If conventional finetuning methods are used, then personalization is achieved, but memory and computational resources are excessive for on-device deployment
Solution Approach 1:
The patent extracts only the essential neural network layers required for personalization by removing non-essential layers from the full network. This creates a hollowed neural network that enables on-device personalization with significantly reduced memory resources, making the technology feasible for deployment on devices with limited computational capacity while maintaining effective personalization capability.
Solution Approach 2:
The patent segments the neural network architecture to separate and process only the essential layers needed for personalization tasks. By dividing the network into critical and non-critical components, the system achieves on-device personalization with reduced memory and computational resource requirements, enabling adaptability without excessive resource consumption.
4Adaptability or versatility
If conventional finetuning methods are used, then personalization is achieved, but computational resources are excessive for on-device deployment
Solution Approach 1:
The patent extracts and removes non-essential neural network layers, creating a hollowed network that enables personalization with reduced computational resources. This extraction approach makes on-device deployment feasible by eliminating unnecessary computational complexity while preserving the personalization capability through the essential layers.
Solution Approach 2:
The patent applies partial action by processing data through only the essential layers during personalization finetuning, rather than through the complete network. This partial processing approach reduces computational resource requirements to levels suitable for on-device deployment while maintaining effective personalization capability.
Data Source
AI summary
Systems and techniques are described herein for training and using a machine-learning model (e.g., a neural network). For example, a computing device can: process, using a first trained neural network, data specific to a user to obtain intermediate activation data representing the data, the first trained neural network comprising a plurality of neural network layers; process, using a second trained neural network, the intermediate activation data to generate an output representing the data, the second trained neural network comprising a subset of neural network layers from the plurality of neural network layers of the first trained neural network; determine a loss based on the output; and update parameters of the second trained neural network based on the loss.