Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

When a computational task tolerates a relaxation of its specification or when an algorithm
tolerates the effects of noise in its execution, hardware, system software, and programming …

Deep Neural Networks (DNNs) are widely being adopted for safety-critical applications, eg,
healthcare and autonomous driving. Inherently, they are considered to be highly error …

In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to
their unmatchable performance in several applications, such as image processing, computer …

Machine Learning (ML) is making a strong resurgence in tune with the massive generation
of unstructured data which in turn requires massive computational resources. Due to the …

We empirically evaluate an undervolting technique, ie, underscaling the circuit supply
voltage below the nominal level, to improve the power-efficiency of Convolutional Neural …

Deep neural networks (DNNs) have become one of the enabling technologies in many
safety-critical applications, eg, autonomous driving and medical image analysis. DNN …

Deep neural networks usually possess a high overall resilience against errors in their
intermediate computations. However, it has been shown that error resilience is generally not …

Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as
autonomous vehicles, demands a reliable and efficient execution on hardware. The design …

Low voltage architecture and design are key enablers of high throughput per watt in
heterogeneous, accelerator-rich many-core designs. However, such low voltage operation …