The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. Due to
its unique features, the GPU continues to remain the most widely used accelerator for DL …

There is a growing interest in dataset generation recently due to the superior generative
capacity of large pre-trained language models (PLMs). In this paper, we study a flexible and …

In recent years, larger and deeper models are springing up and continuously pushing state-
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …

Modern neural networks are increasingly bottlenecked by the limited capacity of on-device
GPU memory. Prior work explores dropping activations as a strategy to scale to larger neural …

Recent advances in Information Retrieval utilise energy-intensive hardware to produce state-
of-the-art results. In areas of research highly related to Information Retrieval, such as Natural …

Advances in deep learning (DL) have prompted the development of cloud-hosted DL-based
media applications that process video and audio streams in real-time. Such applications …

Video cameras are among the most ubiquitous sensors in the Internet-of-Things. Video and
audio applications, such as cross-camera activity detection, avatar extraction or language …

As research and practice in artificial intelligence (AI) grow in leaps and bounds, the
resources necessary to sustain and support their operations also grow at an increasing …

DNN inference is becoming prevalent for many real-world applications. Current machine
learning frameworks usually schedule inference tasks with the goal of optimizing throughput …

Transformer-based models have achieved tremendous success in sequential
recommendation (SR), but they suffer from consuming excessive computational resources …