Neural-Hardware Architecture Search Using Controller Selection
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Solution Overview
Problem
Current industry approaches for optimizing AI algorithms on hardware are manual, lack generalizability, and struggle to converge effectively across a broad range of hardware accelerators, particularly for neural networks, and do not allow for arbitrary design and multi-workload optimization.
Innovation Solution
A system for automatic optimization of neural network and hardware search spaces that integrates optimization controllers and search space analyzers, enabling end-to-end automated generalizable AI optimization without requiring manual expertise or domain knowledge, by sampling search subspaces and evaluating candidate controllers for optimal design selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual optimization approaches are used for hardware and neural network architecture, then expertise in both fields can achieve optimized performance, but the process is inefficient and costly with high engineering costs
Solution Approach 1:
The system enables self-service optimization by automatically performing architecture search and hardware co-optimization without requiring manual expert intervention. The automated framework evaluates design spaces and selects optimal configurations independently, eliminating the need for human experts to manually tune both hardware and neural network parameters while maintaining high optimization quality.
Solution Approach 2:
The patent replaces manual expert optimization (mechanical human effort) with an automated computational system. The framework uses algorithmic approaches to substitute human expertise in hardware and neural network design, transforming the manual optimization process into an automated computational procedure that efficiently explores design spaces and identifies optimal architectures.
2Manufacturing precision
If hardware-aware model architecture search is used, then optimization on a single hardware platform is improved, but generalizability across a broad range of hardware accelerators is limited
Solution Approach 1:
The system achieves universality by designing a unified optimization framework that simultaneously handles multiple hardware platforms. The framework evaluates architectures across diverse hardware accelerators using a common methodology, enabling the same system to optimize for different hardware targets without requiring separate manual optimization processes for each platform.
Solution Approach 2:
The patent introduces dynamics by making the optimization process adaptable to different hardware configurations. The framework dynamically adjusts its evaluation based on the target hardware characteristics, allowing it to converge effectively across a broad range of hardware accelerators while maintaining platform-specific optimization quality through automated adaptation.
3Power
If custom and configurable accelerators are designed for deep learning, then hardware performance is improved, but the design process becomes more complex and requires manual tuning
Solution Approach 1:
The system applies segmentation by breaking down the complex accelerator design process into manageable components. The framework separately optimizes hardware architecture parameters and neural network architectures, then integrates them through automated evaluation. This segmentation allows custom configurable accelerators to achieve high performance while reducing overall design complexity through systematic decomposition of the optimization task.
4Reliability
If manual optimization approaches are used, then specific narrow challenges can be addressed, but the approach is not generalizable to broader hardware and model design challenges
Solution Approach 1:
The patent introduces an intermediary automated framework that mediates between specific hardware challenges and general solutions. The system acts as an intermediary layer that translates specific optimization requirements into unified evaluation criteria, enabling reliable resolution of specific challenges while maintaining generalizability across diverse hardware and model design problems through a standardized automated approach.
Data Source
AI summary
An architecture search system evaluates a search space of neural network and hardware architectures with a plurality of candidate controllers. Each controller attempts to identify an optimized architecture using a different optimization algorithm. To identify a controller for the search space, the architecture search system samples subspaces of the search space having a portion of the neural network search space and a portion of the hardware search space. For each subspace, candidate controllers are scored with respect to the optimized design determined by the respective candidate controllers. Using the scores for the various candidate controllers across the sampled subspaces, a controller is selected to optimize the overall network architecture search space.