Hardware Acceleration for Explainable Machine Learning Interpretation
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Solution Overview
Problem
Existing machine learning techniques lack explainability, making them inefficient for real-time applications and incompatible with accelerated hardware components like FPGAs, GPUs, and TPUs, leading to suboptimal performance and energy inefficiency in providing outcome interpretation.
Innovation Solution
An efficient framework for explainable machine learning is developed, utilizing synergies between convolution operations and Fourier transform operations on hardware accelerators like FPGAs, GPUs, and TPUs, transforming explainable ML procedures into matrix-based operations for parallel processing across multiple cores, enabling real-time or near real-time outcome interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing explainable ML methods are used, then explainability is provided, but interpretation time and energy consumption are excessively high
Solution Approach 1:
The patent replaces traditional software-based explainable ML computations with hardware accelerator implementations. By mapping explainable ML operations to dedicated hardware circuits, the system achieves parallel processing capabilities that dramatically reduce interpretation time while maintaining explainability. The hardware accelerators execute matrix operations and Fourier transforms that are fundamental to explainable ML methods like SHAP and integrated gradients.
Solution Approach 2:
The patent segments the explainable ML computation process into distinct operational phases that can be executed in parallel on hardware accelerators. By dividing the computation of explanation metrics into independent parallel tasks, the system processes multiple data points simultaneously, reducing overall interpretation time without sacrificing explainability quality.
2Loss of information
If existing explainable ML methods are used, then outcome interpretation is provided, but energy consumption is excessively high
Solution Approach 1:
The patent substitutes energy-intensive software-based computations with efficient hardware accelerator operations. Dedicated hardware circuits perform matrix multiplications and Fourier transforms with significantly lower energy consumption compared to general-purpose processors, enabling explainable ML to run efficiently in resource-constrained environments while maintaining full explainability functionality.
3Speed
If explainable ML is implemented in real-time systems, then timely outcome interpretation is achieved, but compatibility with hardware accelerators is insufficient
Solution Approach 1:
The patent designs a universal framework that adapts explainable ML methods to work across multiple hardware accelerator types. By creating hardware-agnostic interfaces and standardized computation patterns, the system achieves real-time processing speeds on diverse hardware platforms including FPGAs, GPUs, and ASICs, thereby improving both speed and hardware compatibility simultaneously.
4Loss of information
If complex computations are performed for explainability, then detailed outcome interpretation is provided, but computational efficiency decreases
Solution Approach 1:
The patent segments complex explainable ML computations into modular operations that can be executed efficiently on hardware accelerators. By breaking down detailed interpretation calculations into discrete parallelizable tasks, the system maintains high computational detail while achieving improved efficiency through hardware-accelerated parallel execution.
Solution Approach 2:
The patent implements adaptive computation that performs detailed explainable ML operations only when necessary, rather than always executing full computational sequences. This selective approach maintains interpretation detail quality while improving overall computational efficiency by avoiding unnecessary calculations in real-time systems.
Data Source
AI summary
Various embodiments provide methods, apparatuses, computer program products, systems, and/or the like for an efficient framework that enables explainability machine learning for various machine learning-based tasks. In various embodiments, the framework for explainable ML is configured for acceleration and efficient computing using hardware accelerators. To provide acceleration of explainable ML, various embodiments exploit synergies between convolution operations for data objects (e.g., matrix, images, tensors, arrays) and Fourier transform operations, and various embodiments apply these synergies in hardware accelerators configured to perform such operations. Accordingly, various embodiments of the present disclosure may be applied in order to provide real-time or near real-time outcome interpretation in various machine learning-based tasks. Extensive experimental evaluations demonstrate that various embodiments described herein can provide drastic improvement in interpretation time (e.g., 39× on average) as well as energy efficiency (e.g., 69× on average) compared to existing techniques.