Running multiple deep neural networks (DNNs) in parallel has become an emerging
workload in both edge devices, such as mobile phones where multiple tasks serve a single …

Embedding retrieval is a crucial task for recommendation systems. Graph-based
approximate nearest neighbor search (GANNS) is the most commonly used method for …

Large-scale deep neural network (DNN) accelerators are poised to facilitate the concurrent
processing of diverse DNNs, imposing demanding challenges on the interconnection fabric …

The need to efficiently execute different Deep Neural Networks (DNNs) on the same
computing platform, coupled with the requirement for easy scalability, makes Multi-Chip …

This paper presents a novel approach for executing the inference of a network of pre-trained
deep neural networks (DNNs) on commercial-off-the-shelf devices that are deployed at the …

Deep learning Processor Unit (DPU) is a highly configurable CNN accelerator that supports
a variety of CNNs and can be implemented with multiple instances on the same FPGA. Many …

As deep learning (DL) technologies become ubiquitous, GPU clusters are deployed for
inference tasks with consistent service level objectives (SLOs). Efficiently utilizing multiple …

With the advancement of artificial intelligence, the collaboration of multiple deep neural
networks (DNNs) has been crucial to existing embedded systems and cloud systems …

The demand for executing Deep Neural Networks (DNNs) with low latency and minimal
power consumption at the edge has led to the development of advanced heterogeneous …

The significant advancements of deep neural networks (DNNs) in a wide range of
application domains have spawned the need for more specialized, sophisticated solutions in …