Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble

TL;DR

This paper introduces an error-resilient machine learning approach using classifier ensembles, demonstrating superior robustness over centralized models like SVM in near-threshold voltage conditions through architectural error modeling and a novel voting technique.

Contribution

It proposes a classifier ensemble framework for error resilience in NTV environments and introduces an error weighted voting method to further improve robustness.

Findings

RF-based architectures are more robust than SVM in NTV conditions.

RF maintains high detection accuracy with minimal variation under timing errors.

Error weighted voting significantly reduces detection accuracy variation.

Abstract

In this paper, we present the design of error-resilient machine learning architectures by employing a distributed machine learning framework referred to as classifier ensemble (CE). CE combines several simple classifiers to obtain a strong one. In contrast, centralized machine learning employs a single complex block. We compare the random forest (RF) and the support vector machine (SVM), which are representative techniques from the CE and centralized frameworks, respectively. Employing the dataset from UCI machine learning repository and architectural-level error models in a commercial 45 nm CMOS process, it is demonstrated that RF-based architectures are significantly more robust than SVM architectures in presence of timing errors due to process variations in near-threshold voltage (NTV) regions (0.3 V - 0.7 V). In particular, the RF architecture exhibits a detection accuracy (P_{det}) that varies by 3.2% while maintaining a median P_{det} > 0.9 at a gate level delay variation of 28.9% . In comparison, SVM exhibits a P_{det} that varies by 16.8%. Additionally, we propose an error weighted voting technique that incorporates the timing error statistics of the NTV circuit fabric to further enhance robustness. Simulation results confirm that the error weighted voting achieves a P_{det} that varies by only 1.4%, which is 12X lower compared to SVM.