Recent advances in artificial intelligence have enhanced the abilities of mobile robots in
dealing with complex and dynamic scenarios. However, to enable computationally intensive …

With the widespread adoption of deep neural networks (DNNs) across applications, there is
a growing demand for DNN deployment solutions that can seamlessly support multi-tenant …

Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-
driving vehicles, unmanned aerial vehicles (UAVs), and security robots. However, real-world …

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 …

The next generation of artificial intelligence (AI) systems will have multi-deep neural network
(multi-DNN) workloads as their core. Large-scale deployment of AI services and integration …

With DNN turning into the backbone of AI cloud services and propelling the emergence of
INFerence-as-a-Service (INFaaS), DNN-specific accelerators have become the …

Convolutional Neural Networks (CNNs) are the state-of-the-art solution for many deep
learning applications. For maximum scalability, their computation should combine high …

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 acceleration of deep-learning kernels in hardware relies on matrix multiplications that
are executed efficiently on Systolic Arrays (SA). To effectively trade off deep-learning …

Today, various domain-specific convolutional neural network (CNN) accelerators are
deployed in large-scale systems to satisfy the massive computational demands of current …