Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be
utilized in various applications due to their capability to learn how to solve complex …

The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …

While the role of Deep Neural Networks (DNNs) in a wide range of safety-critical
applications is expanding, emerging DNNs experience massive growth in terms of …

The stringent requirements for the Deep Neural Networks (DNNs) accelerator's reliability
stand along with the need for reducing the computational burden on the hardware platforms …

We propose a design methodology to facilitate fault tolerance of deep learning models. First,
we implement a many-core fault-tolerant neuromorphic hardware design, where neuron and …

Spiking neural networks have made breakthroughs in computer vision by lending
themselves to neuromorphic hardware. However, the neuromorphic hardware lacks …

Deep Neural Network (DNN) hardware accelerators are essential in a spectrum of safety-
critical edge-AI applications with stringent reliability, energy efficiency, and latency …

Approximate computing is known for enhancing deep neural network accelerators' energy
efficiency by introducing inexactness with a tolerable accuracy loss. However, small …

This paper aims to comprehensively explore challenges and opportunities to design highly
efficient Neural Network (NN) systems through Approximate Computing (AxC) techniques …

Approximate computing is known for its effectiveness in improvising the energy efficiency of
deep neural network (DNN) accelerators at the cost of slight accuracy loss. Very recently, the …