Emerging AI-enabled applications such as augmented and virtual reality (AR/VR) leverage
multiple deep neural network (DNN) models for various sub-tasks such as object detection …

There is a growing interest in custom spatial accelerators for machine learning applications.
These accelerators employ a spatial array of processing elements (PEs) interacting via …

Abstract Convolutional Neural Networks (CNNs) have a wide range of applications due to
their superior performance in image and pattern classification. However, the performance of …

Deep neural network (DNN) models continue to grow in size and complexity, demanding
higher computational power to enable real-time inference. To efficiently deliver such …

Relational data present in real world graph representations demands for tools capable to
study it accurately. In this regard Graph Neural Network (GNN) is a powerful tool, wherein …

As model size and the number of layers increase, Deep Neural Networks (DNNs) demand
enormous computational power and throughput to meet exceedingly high prediction …

Data movement is a key factor impacting the performance of hardware accelerators. In a
complex SoC architecture, multiple accelerators compete for accessing the resources of on …

In NoC-based neural network accelerators, many-to-one and many-to-many are prevalent
traffic patterns. In these traffic patterns, there exists a need for communication …

Advanced Artificial Intelligence (AI) systems that process vast feature sets can be designed
using Deep Neural Networks (DNN). The popularity and prevalence of DNN based tasks are …

In recent years, Network-on-Chip (NoC) has emerged as a promising solution for addressing
a critical performance bottleneck encountered in designing large-scale multi-core systems …