  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.
