Optimizing Machine Learning Models with Data-level Approximate Computing: The Role of Diverse Sampling, Precision Scaling, Quantization and Feature Selection Strategies

Optimizing Machine Learning Models with Data-level Approximate Computing: The Role of Diverse Sampling, Precision Scaling, Quantization and Feature Selection Strategies

Efficiency, low-power consumption, and real-time processing in embedded machine learning implementations are critical, particularly for models deployed in environments with large-scale data processing and resource-constrained environments. This paper investigates the application of approximate compu...

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Bibliographic Details
Main Authors: Ayad M. Dalloo, Amjad J. Humaidi
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Results in Engineering
Subjects:
Machine Learning
Approximate Computing
Sampling, quantization and precision scaling
Critical Applications
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123024017031
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