Neural Deep Equilibrium Solver Hypersolver Inference Speed
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Solution Overview
Problem
Deep equilibrium (DEQ) neural networks face inefficiencies in inference speed due to the computational overhead of iterative fixed-point solvers, which can render them slower than state-of-the-art explicit networks, and previous attempts to speed up inference have compromised model accuracy.
Innovation Solution
A learnable, content-aware hypersolver is introduced, comprising a learned initializer and a generalized parameterized version of Anderson mixing, which estimates initial values and performs iterative updates as an input-dependent temporal process, allowing for faster inference without degrading model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative fixed-point solvers are used in DEQ neural networks, then model accuracy is maintained, but inference speed deteriorates
Solution Approach 1:
The learned initializer performs preliminary action by estimating initial values that are closer to the fixed-point solution before the iterative solver begins. This preliminary estimation reduces the number of iterations required to converge, thereby improving inference speed while maintaining model accuracy.
Solution Approach 2:
The patent replaces the traditional mechanical iterative solver with a hybrid approach that uses a learned initializer (neural network component) to predict initial values. This substitution of the mechanical iteration process with a learned prediction mechanism reduces computational overhead and accelerates inference.
2Speed
If previous attempts to speed up inference are made, then inference speed improves, but model accuracy deteriorates
Solution Approach 1:
The generalized parameterized Anderson mixing acts as an intermediary between the initializer and the fixed-point solution. It refines the initial estimate through a controlled number of iterative steps using learned parameters, ensuring that the approximation remains accurate while still achieving faster convergence than traditional solvers.
Solution Approach 2:
The patent changes parameters by using learned parameters in the Anderson mixing process instead of fixed traditional solver parameters. These learned parameters are optimized during training to balance convergence speed and accuracy, allowing the system to achieve both faster inference and maintained model accuracy.
3Speed
If a learned initializer and Anderson mixing are used, then inference speed improves, but device complexity increases
Solution Approach 1:
The solver is segmented into two distinct components: a learned initializer that generates initial estimates and a generalized Anderson mixing that performs refinement iterations. This segmentation allows each component to be optimized independently and makes the overall system more manageable and efficient despite the increased complexity.
Solution Approach 2:
The generalized parameterized Anderson mixing serves multiple functions: it refines initial estimates, adapts to different DEQ architectures, and can be configured with varying numbers of iterations. This multi-functionality reduces the need for multiple specialized solvers and simplifies the overall system architecture despite the enhanced capabilities.
Data Source
AI summary
Systems and methods for operating a deep equilibrium (DEQ) model in a neural network are disclosed. DEQs solve for a fixed point of a single nonlinear layer, which enables decoupling the internal structure of the layer from how the fixed point is actually computed. This disclosure discloses that such decoupling can be exploited while substantially enhancing this fixed point computation using a custom neural solver. The solver disclosed herein uses a parameterized network to both guess an initial value of the optimization and perform iterative updates in a method that can be trained end-to-end