An area and energy efficient design of domain-wall memory-based deep convolutional neural networks using stochastic computing
With recent trend of wearable devices and Internet of Things (IoTs), it becomes attractive to
develop hardware-based deep convolutional neural networks (DCNNs) for embedded …
develop hardware-based deep convolutional neural networks (DCNNs) for embedded …
An event-driven neuromorphic system with biologically plausible temporal dynamics
Driven by the expanse of Internet of Things (IoT) and Cyber-Physical Systems (CPS), there
is an increasing demand to process streams of temporal data on embedded devices with …
is an increasing demand to process streams of temporal data on embedded devices with …
Adaptive wireless power transfer beam scheduling for non-static IoT devices using deep reinforcement learning
In this article, we study wireless power transfer (WPT) beam scheduling for a system which
consists of IoT devices and a power beacon (PB) using switched beamforming. In such a …
consists of IoT devices and a power beacon (PB) using switched beamforming. In such a …
Darb: A density-adaptive regular-block pruning for deep neural networks
The rapidly growing parameter volume of deep neural networks (DNNs) hinders the artificial
intelligence applications on resource constrained devices, such as mobile and wearable …
intelligence applications on resource constrained devices, such as mobile and wearable …
Architecting green artificial intelligence products: Recommendations for sustainable ai software development and evaluation
MA Alloghani - Artificial Intelligence and Sustainability, 2023 - Springer
With unabated global warming and climate change, the concept of Green Artificial
Intelligence has emerged in which companies in the information and communication sector …
Intelligence has emerged in which companies in the information and communication sector …
[PDF][PDF] On multi-class aerial image classification using learning machines
Q Memon, N Valappil - Computer Vision and Recognition Systems …, 2021 - researchgate.net
Computer vision and image processing are excelling in the field of segmentation, feature
extraction and object detection from image data. In this decade, machine learning …
extraction and object detection from image data. In this decade, machine learning …
Exploring GPU acceleration of deep neural networks using block circulant matrices
Abstract Training a Deep Neural Network (DNN) is a significant computing task since it
places high demands on computing resources and memory bandwidth. Many approaches …
places high demands on computing resources and memory bandwidth. Many approaches …
Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings
Ultra-wideband technology has become increasingly popular for indoor localization and
location-based services. This has led recent advances to be focused on reducing the …
location-based services. This has led recent advances to be focused on reducing the …
A sot-mram-based processing-in-memory engine for highly compressed dnn implementation
The computing wall and data movement challenges of deep neural networks (DNNs) have
exposed the limitations of conventional CMOS-based DNN accelerators. Furthermore, the …
exposed the limitations of conventional CMOS-based DNN accelerators. Furthermore, the …
[PDF][PDF] Optimization scheme for intelligent master controller with collaboratives energy system
This paper explores the use of deep learning to optimize the performance of a peer-to-peer
energy system with an intelligent master controller. The goal addresses inefficiencies …
energy system with an intelligent master controller. The goal addresses inefficiencies …