scholarly journals A sorting image sensor: an example of massively parallel intensity-to-time processing for low-latency computational sensors

2002 ◽  
Author(s):  
V. Brajovic ◽  
T. Kanade
Keyword(s):  
Image Sensor ◽  
Massively Parallel ◽  
Low Latency ◽  
Time Processing ◽  
Computational Sensors

Edge Cloud

2019 ◽  
pp. 107-131
Author(s):  
Lucia Agnes Beena Thomas
Keyword(s):  
Cloud Computing ◽  
Virtual Reality ◽  
Cost Saving ◽  
New Technologies ◽  
Smart Cities ◽  
Low Latency ◽  
Battery Life ◽  
Real Time Processing ◽  
Time Processing ◽  
Autonomous Cars

With the proliferation of new technologies such as augmented and virtual reality, autonomous cars, 5G networks, drones, and IOT with smart cities, consumers of cloud computing are becoming the producers of data. Large volume of data is being produced at the edge of the network. This scenario insists the need for efficient real-time processing and communication at the network edge. Cloud capabilities must be distributed across the network to form an edge cloud, which places computing resources where the traffic is at the edge of the network. Edge cloud along with 5G services could also glint the next generation of robotic manufacturing. The anticipated low latency requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy are also inscribed by edge cloud. A number of giants like Nokia, AT&T, and Microsoft have emerged in the market to support edge cloud. This chapter wraps the features of edge cloud, the driving industries that are providing solutions, the use cases, benefits, and the challenges of edge cloud.

An Efficient Implementation of Low-Latency Two-Dimensional Gaussian Smoothing Filter using Approximate Carry-Save Adder

2020 ◽  
pp. 2150021
Author(s):  
Hadise Ramezani ◽  
Majid Mohammadi ◽  
Amir Sabbagh Molahoseini
Keyword(s):  
Real Time ◽  
Filter Design ◽  
Low Latency ◽  
Gaussian Filter ◽  
Two Dimensional ◽  
Gaussian Smoothing ◽  
Real Time Processing ◽  
Time Processing ◽  
Smoothing Filter ◽  
Gaussian Filters

The two-dimensional Gaussian smoothing filter (2D-GSF) is one of the most useful techniques in image processing. Since the 2D-GSF requires high computational resources, its efficient design and implementation are critical in real-time processing purposes. Approximate computing is a new method that can be used to increase the performance of 2D Gaussian filter design with low computing overhead on field-programmable gate arrays (FPGAs). This study aims to provide a low-latency Gaussian filter architecture on FPGA such that it can be used in real-time processing applications. In this regard, accurate and approximate carry-save adders (CSAs) have been used in adder tree-based Gaussian filters. In our proposed method, we use two approximation steps: in the first step, we use an approximation structure named Speed–Power–Area–Accuracy for Gaussian filter design and in the second stage, we use approximate CSAs to convert adder-tree structures that are used in Gaussian filter, and as a result, we have significantly reduced the delay. The results of simulation and implementation show that the latency has reduced in a 3[Formula: see text] 3 2D-GSF architecture up to 22% using proposed accurate CSAs and 45% using proposed approximate CSAs, compared to existing Gaussian filters with an adder tree structure.

Time Processing and OCPD: How Task Structure Impacts Performance

2008 ◽  
Author(s):  
Meghan E. Wright ◽  
Deana Davalos ◽  
Carly Yadon ◽  
Kelsey Keener
Keyword(s):  
Task Structure ◽  
Time Processing

Small Scale and Low Latency Oriented Neural Network Physical Unclonable Function and its Evaluation

2020 ◽  
Vol 140 (12) ◽  
pp. 1297-1306
Author(s):  
Shu Takemoto ◽  
Kazuya Shibagaki ◽  
Yusuke Nozaki ◽  
Masaya Yoshikawa
Keyword(s):  
Neural Network ◽  
Small Scale ◽  
Low Latency ◽  
Physical Unclonable Function
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