Configurable Heterogeneous MPSoC Architecture Exploration Using Abstraction Levels

2008 ◽  
Author(s):  
Hao Shen ◽  
Patrice Gerin ◽  
Fr P
Keyword(s):  
Architecture Exploration ◽  
Abstraction Levels ◽  
Heterogeneous Mpsoc

A Fast Architecture Exploration Method for High Throughput IEEE 802.11e MAC Implementation Using SystemC

2010 ◽  
Vol E93-B (10) ◽  
pp. 2833-2836
Author(s):  
Sung-Rok YOON ◽  
Min Li HUANG ◽  
Sangho SEO ◽  
Hiroshi OCHI ◽  
Sin-Chong PARK
Keyword(s):  
High Throughput ◽  
Ieee 802.11E ◽  
Architecture Exploration

scholarly journals fenicsR13

2021 ◽  
Vol 47 (2) ◽  
pp. 1-29
Author(s):  
Lambert Theisen ◽  
Manuel Torrilhon
Keyword(s):  
Gas Flow ◽  
Differential Operators ◽  
Mixed Finite Element ◽  
Test Cases ◽  
Typical Application ◽  
Flow Models ◽  
Computing Platform ◽  
Abstraction Levels ◽  
Variational Formulations ◽  
Rarefaction Effects

We present a mixed finite element solver for the linearized regularized 13-moment equations of non-equilibrium gas dynamics. The Python implementation builds upon the software tools provided by the FEniCS computing platform. We describe a new tensorial approach utilizing the extension capabilities of FEniCS’ Unified Form Language to define required differential operators for tensors above second degree. The presented solver serves as an example for implementing tensorial variational formulations in FEniCS, for which the documentation and literature seem to be very sparse. Using the software abstraction levels provided by the Unified Form Language allows an almost one-to-one correspondence between the underlying mathematics and the resulting source code. Test cases support the correctness of the proposed method using validation with exact solutions. To justify the usage of extended gas flow models, we discuss typical application cases involving rarefaction effects. We provide the documented and validated solver publicly.

Architecture Exploration for a Reconfigurable Architecture Template

2005 ◽  
Vol 22 (2) ◽  
pp. 90-101 ◽  
Author(s):  
Bingfeng Mei ◽  
A. Lambrechts ◽  
D. Verkest ◽  
J. Mignolet ◽  
R. Lauwereins
Keyword(s):  
Reconfigurable Architecture ◽  
Architecture Exploration

On the Statistical Memory Architecture Exploration and Optimization

2015 ◽  
Author(s):  
Charalampos Antoniadis ◽  
Georgios Karakonstantis ◽  
Nestor Evmorfopoulos ◽  
Andreas Burg ◽  
George Stamoulis
Keyword(s):  
Memory Architecture ◽  
Architecture Exploration

IMAGE RECOGNITION BY INTEGRATION OF CONNECTIONIST AND SYMBOLIC APPROACHES

1995 ◽  
Vol 09 (03) ◽  
pp. 485-515 ◽  
Author(s):  
F. ROLI ◽  
S. B. SERPICO ◽  
G. VERNAZZA
Keyword(s):  
Image Recognition ◽  
Magnetic Resonance Image ◽  
Contextual Information ◽  
Recognition System ◽  
Recognition Problem ◽  
Training Process ◽  
Neural Architecture ◽  
Recognition Phase ◽  
Abstraction Levels

This paper presents a methodology for integrating connectionist and symbolic approaches to 2D image recognition. The proposed integration paradigm exploits the synergy of the two approaches for both the training and the recognition phases of an image recognition system. In the training phase, a symbolic module provides an approximate solution to a given image-recognition problem in terms of symbolic models. Such models are hierarchically organized into different abstraction levels, and include contextual descriptions. After mapping such models into a complex neural architecture, a neural training process is carried out to optimize the solution of the recognition problem. The so-obtained neural networks are used during the recognition phase for pattern classification. In this phase, the role of symbolic modules consists of managing complex aspects of information processing: abstraction levels, contextual information, and global recognition hypotheses. A hybrid system implementing the proposed integration paradigm is presented, and its advantages over single approaches are assessed. Results on Magnetic Resonance image recognition are reported, and comparisons with some well-known classifiers are made.

Hardware Architecture Exploration for Deep Neural Networks

2021 ◽  
Author(s):  
Wenqi Zheng ◽  
Yangyi Zhao ◽  
Yunfan Chen ◽  
Jinhong Park ◽  
Hyunchul Shin
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Hardware Architecture ◽  
Architecture Exploration

System-Level Specification and Architecture Exploration : an Avionics Codesign Application

2001 ◽  
pp. 111-120
Author(s):  
François Cloute ◽  
Jean-Noël Contensou ◽  
Daniel Esteve ◽  
Pascal Pampagnin ◽  
Philippe Pons ◽  
...  
Keyword(s):  
System Level ◽  
Architecture Exploration
Sign in / Sign up

Export Citation Format

Share Document