Today's cloud data centers are often distributed geographically to provi...
With the continuous increase in the size and complexity of machine learn...
Network-on-chip (NoC) architectures provide a scalable, high-performance...
Graph neural networks (GNNs) have emerged as a powerful approach for
mod...
Indoor localization plays a vital role in applications such as emergency...
The integration of silicon photonics (SiPh) and phase change materials (...
Transformer neural networks are rapidly being integrated into
state-of-t...
Emerging AI applications such as ChatGPT, graph convolutional networks, ...
The next generation of computer engineers and scientists must be profici...
To enable emerging applications such as deep machine learning and graph
...
Machine learning (ML) algorithms are increasingly being integrated into
...
Object detectors used in autonomous vehicles can have high memory and
co...
Wi-Fi fingerprinting-based indoor localization is an emerging embedded
a...
Domain-specific machine learning (ML) accelerators such as Google's TPU ...
Computing systems are tightly integrated today into our professional, so...
This article reviews the landscape of ethical challenges of integrating
...
The dawn of the digital medicine era, ushered in by increasingly powerfu...
Recurrent Neural Networks (RNNs) are used in applications that learn
dep...
Networks-on-chips (NoCs) are an integral part of emerging manycore compu...
2.5D chiplet systems have been proposed to improve the low manufacturing...
Network-on-chip (NoC) architectures rely on buffers to store flits to co...
Parameter quantization in convolutional neural networks (CNNs) can help
...
Embedded computing systems are pervasive in our everyday lives, impartin...
Cloud workloads today are typically managed in a distributed environment...
Smartphones together with RSSI fingerprinting serve as an efficient appr...
Modern indoor localization techniques are essential to overcome the weak...
In emerging automotive cyber-physical systems (CPS), accurate environmen...
Silicon-photonic neural networks (SPNNs) have emerged as promising succe...
Compared to electronic accelerators, integrated silicon-photonic neural
...
Object detection is a computer vision task that has become an integral p...
Autonomous vehicles are on the horizon and will be transforming
transpor...
By interconnecting smaller chiplets through an interposer, 2.5D integrat...
Singular-value-decomposition-based coherent integrated photonic neural
n...
We propose a novel hardware-aware magnitude pruning technique for cohere...
Fingerprinting-based indoor localization is an emerging application doma...
Sparse neural networks can greatly facilitate the deployment of neural
n...
Modern vehicles can be thought of as complex distributed embedded system...
Domain specific neural network accelerators have garnered attention beca...
GPS technology has revolutionized the way we localize and navigate outdo...
Cloud service providers are distributing data centers geographically to
...
Indoor localization services are a crucial aspect for the realization of...
The approximate computing paradigm advocates for relaxing accuracy goals...
Domain-specific neural network accelerators have seen growing interest i...
Deep learning has led to unprecedented successes in solving some very
di...
Due to amount of data involved in emerging deep learning and big data
ap...
The compact size and high wavelength-selectivity of microring resonators...
Today's vehicles are complex distributed embedded systems that are
incre...
The approximate computing paradigm advocates for relaxing accuracy goals...