Hardware-Aware Neural Network Architecture Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural network architectures face challenges in achieving efficient hardware implementation, particularly in terms of latency, as they often prioritize model quality over practical considerations like real-time operation and hardware constraints.
Innovation Solution
The method involves determining a search space for neural network architectures based on measures that account for hardware constraints, such as matrix operations, layer input and output data, and vector operations, and applying constraints like limited stages, convolution layers, normalization, and skip connections to find efficient architectures that balance latency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network architectures prioritize model quality with larger model sizes, then accuracy is improved, but latency increases and real-time operation becomes impractical
Solution Approach 1:
The patent changes the parameters used to evaluate and select neural network architectures by introducing hardware-aware metrics that consider both accuracy and latency. The search space is defined using parameters that reflect hardware constraints, allowing the system to find architectures that optimize the trade-off between model quality and operational speed.
Solution Approach 2:
The patent implements a dynamic search process where the architecture selection is not static but adapts to hardware constraints. The system dynamically evaluates candidate architectures based on their performance characteristics and hardware compatibility, selecting the optimal configuration for real-time operation.
2Measurement precision
If neural network architectures are designed without considering hardware constraints, then model quality can be improved, but hardware efficiency and real-time operation capability deteriorate
Solution Approach 1:
The patent applies preliminary action by defining the search space with hardware constraints before the actual architecture search begins. The system pre-establishes the boundaries and criteria for evaluation based on hardware capabilities, ensuring that all candidate architectures are inherently suitable for efficient hardware implementation.
Solution Approach 2:
The patent creates a universal framework that can evaluate neural network architectures across different hardware platforms. The hardware-aware search space definition and evaluation metrics are designed to be adaptable to various hardware configurations, making the approach universally applicable to different processing units.
3Adaptability or versatility
If the search space for neural network architectures includes all possible configurations, then comprehensive evaluation is possible, but search complexity and computational cost increase significantly
Solution Approach 1:
The patent extracts only the relevant and necessary architectures from the complete search space by defining constrained search spaces based on hardware awareness. Instead of evaluating all possible configurations, the system extracts candidate architectures that meet specific hardware criteria, significantly reducing the evaluation burden while maintaining comprehensiveness within practical limits.
Data Source
AI summary
A method for searching for one or more neural network (NN) architectures is provided. The method determines a search space comprising a plurality of architectures including one or more blocks, wherein a block of the one or more blocks is formed by one or more NN layers. The determining is based on a measure including: an amount of matrix operations, and/or one of: i) an amount of layer input data and/or layer output data, and ii) an amount of vector operations. The method searches for the one or more NN architectures in the determined search space.