Scientific workloads have traditionally exploited high levels of sparsity to accelerate
computation and reduce memory requirements. While deep neural networks can be made …

Exploiting model sparsity to reduce ineffectual computation is a commonly used approach to
achieve energy efficiency for DNN inference accelerators. However, due to the tightly …

Convolutional Neural Networks (CNNs) are computationally intensive, which limits their
application on mobile devices. Their energy is dominated by the number of multiplies …

Convolutional neural networks (CNNs) have been widely used in image classification and
recognition due to their effectiveness; however, CNNs use a large volume of weight data that …

FPGAs have been an efficient accelerator for CNN inference due to its high performance,
flexibility, and energy-efficiency. To improve the performance of CNNs on FPGAs, fast …

Convolutional Neural Networks (CNNs) are being increasingly used in safety-critical and
high-performance computing systems. As such systems require high levels of resilience to …

As CNNs are being extensively employed in high performance and safety-critical
applications that demand high reliability, it is important to ensure that they are resilient to …

In recent years, a variety of accelerators on FPGAs have been proposed to speed up the
convolutional neural network (CNN) in many domain-specific application fields. Besides …

Emergent ReRAM-based accelerators support in-memory computation to accelerate deep
neural network (DNN) inference. Weight matrix pruning of DNNs is a widely used technique …

Abstract Neural Architecture Search (NAS), together with model scaling, has shown
remarkable progress in designing high accuracy and fast convolutional architecture families …