Neural Network IC Architecture Optimization
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Solution Overview
Problem
Integrated circuit (IC) architecture optimization faces challenges with inaccurate output characteristic values from heuristic-based approaches and combinatorial explosion in discrete optimization, leading to inefficient use of computational resources.
Innovation Solution
The system employs an optimization engine with an auto-encoder and regressors to encode discrete IC variables into a latent continuous representation, using continuous optimization techniques to generate improved representations that optimize IC performance, and decode them back into discrete input values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heuristic-based approaches are used to estimate output characteristics, then the optimization process can proceed, but the predicted values are inaccurate and do not fully reflect actual IC performance
Solution Approach 1:
The patent introduces an auto-encoder as an intermediary component that transforms discrete IC architecture variables into a continuous latent representation. This latent space serves as a mediator between the discrete input variables and the regression models, enabling more accurate performance prediction while maintaining optimization efficiency. The auto-encoder learns to compress the discrete variables into a continuous space where gradient-based optimization can be effectively applied.
Solution Approach 2:
The patent changes the parameter space from discrete to continuous by encoding discrete IC architecture variables into continuous latent variables. This parameter transformation allows the use of continuous optimization techniques and gradient-based methods, which significantly improve both prediction accuracy and optimization efficiency compared to traditional discrete heuristic approaches.
2Reliability
If discrete optimization techniques involving exhaustive enumeration are used, then all possible combinations can be evaluated, but computational resources such as memory and computing power are inefficiently used due to combinatorial explosion
Solution Approach 1:
The auto-encoder acts as an intermediary that maps discrete variables to a continuous latent space, enabling the use of efficient continuous optimization algorithms instead of exhaustive discrete enumeration. This transformation maintains optimization completeness by preserving the essential relationships between variables while dramatically reducing computational resource requirements.
Solution Approach 2:
The patent replaces the mechanical exhaustive enumeration process with a continuous optimization approach using gradient descent and neural network-based regression. This substitution eliminates the combinatorial explosion problem by working in a continuous space where optimization can proceed efficiently without evaluating all possible discrete combinations.
3Productivity
If continuous optimization techniques are used in the latent space, then computational resources are used efficiently and prediction accuracy improves, but an additional encoding/decoding process is required
Solution Approach 1:
The auto-encoder serves as the intermediary system that handles the encoding and decoding between discrete and continuous spaces. While this adds a transformation step, the auto-encoder is trained offline and during inference, the additional complexity is minimal compared to the significant gains in optimization efficiency and prediction accuracy achieved through continuous space optimization.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing integrated circuit architectures or compiler designs using an optimization engine. The optimization engine includes an auto-encoder and one or more regressors. Once trained, the optimization engine can encode initial, discrete input values of a set of input characteristics into a continuous domain and use continuous optimization techniques to identify final input values of the set of input characteristics that optimize one or more output characteristics.