Low-power computer vision: Status, challenges, and opportunities
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile
phones have become the primary computing platforms for millions of people. In addition to …
phones have become the primary computing platforms for millions of people. In addition to …
Dac-sdc low power object detection challenge for uav applications
The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC)
in 2018. SDC'18 features a lower power object detection challenge (LPODC) on designing …
in 2018. SDC'18 features a lower power object detection challenge (LPODC) on designing …
CNN-based object detection solutions for embedded heterogeneous multicore SoCs
This paper surveys how to use Convolutional Neural Networks (CNN) to hypothesize object
location and categorization from images or videos in mobile heterogeneous SoCs. Recently …
location and categorization from images or videos in mobile heterogeneous SoCs. Recently …
[LLIBRE][B] Low-power computer vision: improve the efficiency of artificial intelligence
Energy efficiency is critical for running computer vision on battery-powered systems, such as
mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the …
mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the …
Low-power image recognition challenge
Significant progress has been made in recent years using computer programs recognizing
objects in images. Meanwhile, many cameras are embedded in battery-powered systems …
objects in images. Meanwhile, many cameras are embedded in battery-powered systems …
[LLIBRE][B] Exploring the design space of deep convolutional neural networks at large scale
F Iandola - 2016 - search.proquest.com
In recent years, the research community has discovered that deep neural networks (DNNs)
and convolutional neural networks (CNNs) can yield higher accuracy than all previous …
and convolutional neural networks (CNNs) can yield higher accuracy than all previous …
In-memory search with learning to hash based on resistive memory for recommendation acceleration
F Wang, W Zhang, Z Li, N Lin, R Bao, X Xu… - npj Unconventional …, 2024 - nature.com
Similarity search is essential in current artificial intelligence applications and widely utilized
in various fields, such as recommender systems. However, the exponential growth of data …
in various fields, such as recommender systems. However, the exponential growth of data …
2018 low-power image recognition challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee.
org/lpirc) is an annual competition started in 2015. The competition identifies the best …
org/lpirc) is an annual competition started in 2015. The competition identifies the best …
Neural-network-based analysis of EEG data using the neuromorphic TrueNorth chip for brain-machine interfaces
Electroencephalography (EEG) is a noninvasive way to record brain activity by means of
measuring electrical fields arising from neural activation. Being relatively inexpensive, safe …
measuring electrical fields arising from neural activation. Being relatively inexpensive, safe …
A retrospective evaluation of energy-efficient object detection solutions on embedded devices
The field of image and video recognition has been propelled by the rapid development of
deep learning in recent years. With its fascinating accuracy and generalization ability, deep …
deep learning in recent years. With its fascinating accuracy and generalization ability, deep …