Deployment of Artificial Intelligence Models into Edge Devices: A Tutorial Brief
Artificial intelligence (AI) on an edge device has enormous potential, including advanced
signal filtering, event detection, optimization in communications and data compression …
signal filtering, event detection, optimization in communications and data compression …
Deployment of Artificial Intelligence Models on Edge Devices: A Tutorial Brief
Artificial intelligence (AI) on an edge device has enormous potential, including advanced
signal filtering, event detection, optimization in communications and data compression …
signal filtering, event detection, optimization in communications and data compression …
“AI Acceleration on FPGAs”
Artificial Intelligence (AI) applications have become ubiquitous across the computing world,
spanning from large-scale data centers to mobile and Internet of Things (IoT) devices. The …
spanning from large-scale data centers to mobile and Internet of Things (IoT) devices. The …
TRIM: A Design Space Exploration Model for Deep Neural Networks Inference and Training Accelerators
There is increasing demand for specialized hardware for training deep neural networks
(DNNs), both in edge/IoT environments and in high-performance computing systems. The …
(DNNs), both in edge/IoT environments and in high-performance computing systems. The …
Investigation of Deep Learning Models for IoT Devices
S Bhattacharya, D Bhattacharjee - Internet of Things, 2022 - taylorfrancis.com
This chapter explores the current trend and needs for deep neural networks (DNN)
processing in realtime in resource-constrained hardware like IoT and discusses the different …
processing in realtime in resource-constrained hardware like IoT and discusses the different …
Toward Energy-Efficient Machine Learning: Algorithms and Analog Compute-In-memory Hardware
I Chakraborty - 2021 - search.proquest.com
Abstract The 'Internet of Things' has increased the demand for artificial intelligence (AI)-
based edge computing in applications ranging from healthcare monitoring systems to …
based edge computing in applications ranging from healthcare monitoring systems to …
Artificial intelligence in the IoT era: A review of edge AI hardware and software
T Sipola, J Alatalo, T Kokkonen… - 2022 31st Conference …, 2022 - ieeexplore.ieee.org
The modern trend of moving artificial intelligence computation near to the origin of data
sources has increased the demand for new hardware and software suitable for such …
sources has increased the demand for new hardware and software suitable for such …
AI computing reaches for the edge
SS Iyer, V Roychowdhury - Science, 2023 - science.org
Artificial intelligence (AI)—the ability of computers to perform human cognitive functions in
real-world scenarios—requires substantial computation power, energy, and vast datasets …
real-world scenarios—requires substantial computation power, energy, and vast datasets …
On-device learning systems for edge intelligence: A software and hardware synergy perspective
Modern machine learning (ML) applications are often deployed in the cloud environment to
exploit the computational power of clusters. However, this in-cloud computing scheme …
exploit the computational power of clusters. However, this in-cloud computing scheme …
Scaling for edge inference of deep neural networks
Deep neural networks offer considerable potential across a range of applications, from
advanced manufacturing to autonomous cars. A clear trend in deep neural networks is the …
advanced manufacturing to autonomous cars. A clear trend in deep neural networks is the …