Diffusion Model Discrete Data Generation via Analog Bit Representation
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Solution Overview
Problem
Conventional systems face challenges in efficiently generating high-dimensional discrete outputs due to the computational inefficiency and latency associated with autoregressive modeling, while diffusion models operating in continuous space cannot directly generate discrete outputs.
Innovation Solution
The method involves using a diffusion machine learning model to generate discrete data by initializing an analog bit representation of the network output, updating it through multiple iterations, and then converting the continuous values into binary representations to produce discrete output tokens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autoregressive modeling techniques are used to generate discrete outputs, then high quality discrete outputs can be generated, but generating high dimensional discrete outputs requires a large amount of computational resources and increases latency
Solution Approach 1:
The patent introduces an intermediary continuous representation space that bridges discrete inputs and discrete outputs. Instead of directly mapping discrete to discrete (which requires sequential autoregressive steps), the system converts discrete inputs to continuous representations, processes them through a diffusion model, and then converts back to discrete outputs. This intermediary continuous space enables parallel processing while maintaining discrete output quality.
Solution Approach 2:
The patent changes the parameter space from discrete to continuous during processing. By representing data in continuous space during the diffusion process, the system can leverage parallel computation and gradient-based optimization, then transform back to discrete space for final outputs. This parameter transformation resolves the contradiction between discrete output quality and computational efficiency.
2Measurement precision
If autoregressive modeling techniques are used to generate discrete outputs, then high quality discrete outputs can be generated, but the latency of generating discrete outputs increases
Solution Approach 1:
The continuous representation space acts as a mediator that enables simultaneous processing of multiple output tokens. Instead of generating tokens sequentially (one by one), the diffusion model processes all tokens in parallel within the continuous space, dramatically reducing latency while maintaining quality through the intermediary transformation.
Solution Approach 2:
The system performs preliminary conversion of discrete inputs to continuous representations before the main generation process. This preliminary action prepares the data in a format that enables parallel processing, allowing all output tokens to be generated simultaneously rather than sequentially, thus reducing overall latency.
3Productivity
If diffusion models operating in continuous space are used, then computational efficiency can be improved, but they cannot directly generate discrete outputs
Solution Approach 1:
The patent applies parameter changes in both directions: first transforming discrete inputs to continuous parameters for efficient diffusion processing, then transforming the continuous output parameters back to discrete values. This dual transformation enables diffusion models to leverage their computational efficiency while maintaining the ability to generate discrete outputs.
Solution Approach 2:
The continuous representation space serves as an intermediary that allows diffusion models to process data efficiently while still producing discrete outputs. The intermediary space enables the model to benefit from continuous optimization and parallel processing, then convert results to discrete format through thresholding or sampling operations.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a network output of high dimensional data comprising one or more output tokens. In one aspect, a system comprises a neural network system configured to initialize an analog bit representation of the network output comprising a set of continuous numeric values for each of the output tokens. The neural network system generates an updated analog bit representation that comprises a set of updated continuous numeric values. At each of a plurality of update iterations, the neural network system processes a diffusion input comprising the analog bit representation using a diffusion machine learning model to update the analog bit representation.