scholarly journals Hierarchical optimization: A hybrid processing for downlink massive MU‐MIMO mmWave systems

2021 ◽  
Author(s):  
Alvaro Javier Ortega
Keyword(s):  
Hierarchical Optimization

Hierarchical Optimization of Network Resource for Heterogeneous Service in Cloud Scenarios

2018 ◽  
pp. -1--1
Author(s):  
Dong Huang ◽  
Yong Bai ◽  
Jingcheng Liu ◽  
Hongtao Chen ◽  
Jinghua Lin ◽  
...  
Keyword(s):  
Hierarchical Optimization ◽  
Network Resource ◽  
Heterogeneous Service

scholarly journals Towards an Efficient CNN Inference Architecture Enabling In-Sensor Processing

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1955
Author(s):  
Md Jubaer Hossain Pantho ◽  
Pankaj Bhowmik ◽  
Christophe Bobda
Keyword(s):  
Power Consumption ◽  
High Speed ◽  
Image Sensor ◽  
Machine Learning Algorithms ◽  
Hierarchical Optimization ◽  
Computation Method ◽  
Optimization Approach ◽  
Efficient Computation ◽  
Feature Maps ◽  
Dynamic Power

The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challenging task due to the tight constraints on resources and power. Here, the transfer and processing of non-relevant image pixels act as a bottleneck on the entire system. It is possible to overcome this bottleneck by exploiting the high bandwidth available at the sensor interface by designing a CNN inference architecture near the sensor. This paper presents an attention-based pixel processing architecture to facilitate the CNN inference near the image sensor. We propose an efficient computation method to reduce the dynamic power by decreasing the overall computation of the convolution operations. The proposed method reduces redundancies by using a hierarchical optimization approach. The approach minimizes power consumption for convolution operations by exploiting the Spatio-temporal redundancies found in the incoming feature maps and performs computations only on selected regions based on their relevance score. The proposed design addresses problems related to the mapping of computations onto an array of processing elements (PEs) and introduces a suitable network structure for communication. The PEs are highly optimized to provide low latency and power for CNN applications. While designing the model, we exploit the concepts of biological vision systems to reduce computation and energy. We prototype the model in a Virtex UltraScale+ FPGA and implement it in Application Specific Integrated Circuit (ASIC) using the TSMC 90nm technology library. The results suggest that the proposed architecture significantly reduces dynamic power consumption and achieves high-speed up surpassing existing embedded processors’ computational capabilities.

OC-0221: Clinical experience of automated SBRT planning with Constrained Hierarchical Optimization

2020 ◽  
Vol 152 ◽  
pp. S112
Author(s):  
L. Hong ◽  
Y. Zhou ◽  
J. Yang ◽  
J. Mechalakos ◽  
M. Hunt ◽  
...  
Keyword(s):  
Clinical Experience ◽  
Hierarchical Optimization

Sensor Optimization for Fault Diagnosis in Multi-Fixture Assembly Systems With Distributed Sensing

1998 ◽  
Vol 122 (1) ◽  
pp. 215-226 ◽  
Author(s):  
A. Khan ◽  
D. Ceglarek
Keyword(s):  
Optimization Procedure ◽  
Hierarchical Optimization ◽  
Dual Purpose ◽  
Distributed Sensor ◽  
Sensor Optimization ◽  
Multi Level ◽  
Sensor Configuration ◽  
Limited Sensing ◽  
Sheet Metal Assembly ◽  
Efficient Distribution

Sensing for the system-wide diagnosis of dimensional faults in multi-fixture sheet metal assembly presents significant issues of complexity due to the number of levels of assembly and the number of possible faults at each level. The traditional allocation of sensing at a single measurement station is no longer sufficient to guarantee adequate fault diagnostic information for the increased parts and levels of a complex assembly system architecture. This creates a need for an efficient distribution of limited sensing resources to multiple measurement locations in assembly. The proposed methodology achieves adequate diagnostic performance by configuring sensing to provide an optimally distinctive signature for each fault in assembly. A multi-level, two-step, hierarchical optimization procedure using problem decomposition, based on assembly structure data derived directly from CAD files, is used to obtain such a novel, distributed sensor configuration. Diagnosability performance is quantified in the form of a defined index, which serves the dual purpose of guiding the optimization and establishing the diagnostic worth of any candidate sensor distribution. Examples, using a multi-fixture layout, are presented to illustrate the methodology. [S1087-1357(00)70801-X]

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