A performance estimation flow for embedded systems with mixed software/hardware modeling

2011 ◽  
Author(s):  
Joffrey Kriegel ◽  
Alain Pegatoquet ◽  
Michel Auguin ◽  
Florian Broekaert
Keyword(s):  
Embedded Systems ◽  
Performance Estimation ◽  
A Performance

scholarly journals 2018 A Performance Estimation Method for Phase Change Cooling System Using ID Thermal-Fluid Network Analysis

2013 ◽  
Vol 2013.23 (0) ◽  
pp. _2018-1_-_2018-10_
Author(s):  
Izumi NITTA ◽  
Sachio KOBAYASHI ◽  
Hiroki KOBAYASHI ◽  
Masayoshi HASHIMA ◽  
Yuichi SATO ◽  
...  
Keyword(s):  
Network Analysis ◽  
Phase Change ◽  
Cooling System ◽  
Estimation Method ◽  
Performance Estimation ◽  
Fluid Network ◽  
A Performance

scholarly journals CitiusSynapse: A Deep Learning Framework for Embedded Systems

2021 ◽  
Vol 11 (23) ◽  
pp. 11570
Author(s):  
Seungtae Hong ◽  
Hyunwoo Cho ◽  
Jeong-Si Kim
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Parallel Processing ◽  
Embedded Systems ◽  
Structural Characteristics ◽  
Limited Resources ◽  
Learning Framework ◽  
Conventional Methods ◽  
Active Research ◽  
A Performance

As embedded systems, such as smartphones with limited resources, have become increasingly popular, active research has recently been conducted on performing on-device deep learning in such systems. Therefore, in this study, we propose a deep learning framework that is specialized for embedded systems with limited resources, the operation processing structure of which differs from that of standard PCs. The proposed framework supports an OpenCL-based accelerator engine for accelerator deep learning operations in various embedded systems. Moreover, the parallel processing performance of OpenCL is maximized through an OpenCL kernel that is optimized for embedded GPUs, and the structural characteristics of embedded systems, such as unified memory. Furthermore, an on-device optimizer for optimizing the performance in on-device environments, and model converters for compatibility with conventional frameworks, are provided. The results of a performance evaluation show that the proposed on-device framework outperformed conventional methods.

SystemC Platform Modeling for Behavioral Simulation and Performance Estimation of Embedded Systems

2010 ◽  
pp. 219-243 ◽  
Author(s):  
Hector Posadas ◽  
Juan Castillo ◽  
David Quijano ◽  
Victor Fernandez ◽  
Eugenio Villar ◽  
...  
Keyword(s):  
Embedded Systems ◽  
Design Space Exploration ◽  
Performance Metrics ◽  
System Modeling ◽  
Performance Estimation ◽  
Behavioral Simulation ◽  
Close Interaction ◽  
Accurate Performance ◽  
And Performance ◽  
On Chip

Currently, embedded systems make use of large, multiprocessing systems on chip integrating complex application software running on the different processors in close interaction with the application-specific hardware. These systems demand new modeling, simulation, and performance estimation tools and methodologies for system architecture evaluation and design exploration. Recently approved as IEEE 1666 standard, SystemC has proven to be a powerful language for system modeling and simulation. In this chapter, SCoPE, a SystemC framework for platform modeling, SW source-code behavioral simulation and performance estimation of embedded systems is presented. Using SCoPE, the application SW running on the different processors of the platform can be simulated efficiently in close interaction with the rest of the platform components. In this way, fast and sufficiently accurate performance metrics are obtained for design-space exploration.

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