Specialized on-chip accelerators are widely used to improve the energy efficiency of
computing systems. Recent advances in memory technology have enabled near-data …

Image processing is becoming an increasingly important domain for many applications on
workstations and the datacenter that require accelerators for high performance and energy …

Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks
(CNN). However, CNNs have massive compute demands that far exceed the performance …

Point cloud analytics is poised to become a key workload on battery-powered embedded
and mobile platforms in a wide range of emerging application domains, such as …

We show that Deep Convolutional Neural Network (CNN) implementations of computational
imaging tasks exhibit spatially correlated values. We exploit this correlation to reduce the …

Convolutional neural networks (CNNs) have recently demonstrated superior quality for
computational imaging applications. Therefore, they have great potential to revolutionize the …

Transformer network (TN) is a promising model widely used for natural language processing
(NLP), computer vision (CV), and audio processing (AP). However, the large number of …

Data movement between the memory system and computation units is one of the most
critical challenges in designing high performance and energy-efficient computing systems …

Noise in image sensors led to the development of a whole range of denoising filters. A noisy
image can become hard to recognize and often require several types of post-processing …

Image processing algorithms are prime targets for hardware acceleration as they are
commonly used in resource-and power-limited applications. Today's image processing …