Test Vector Ordering For Power Reduction During Transmission of Compressed Test Patterns To Embedded System-On-Chip

Author(s):  
Chandan Giri ◽  
Nikhil Cheruku ◽  
Santanu Chattopadhyay
Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1587
Author(s):  
Duo Sheng ◽  
Hsueh-Ru Lin ◽  
Li Tai

High performance and complex system-on-chip (SoC) design require a throughput and stable timing monitor to reduce the impacts of uncertain timing and implement the dynamic voltage and frequency scaling (DVFS) scheme for overall power reduction. This paper presents a multi-stage timing monitor, combining three timing-monitoring stages to achieve a high timing-monitoring resolution and a wide timing-monitoring range simultaneously. Additionally, because the proposed timing monitor has high immunity to the process–voltage–temperature (PVT) variation, it provides a more stable time-monitoring results. The time-monitoring resolution and range of the proposed timing monitor are 47 ps and 2.2 µs, respectively, and the maximum measurement error is 0.06%. Therefore, the proposed multi-stage timing monitor provides not only the timing information of the specified signals to maintain the functionality and performance of the SoC, but also makes the operation of the DVFS scheme more efficient and accurate in SoC design.


2016 ◽  
Vol 86 (2-3) ◽  
pp. 135-147 ◽  
Author(s):  
Wei Hu ◽  
Hong Guo ◽  
Hongna Geng ◽  
Kai Zhang ◽  
Jun Liu ◽  
...  

2009 ◽  
Vol 4 (10) ◽  
Author(s):  
Wei Hu ◽  
Tianzhou Chen ◽  
Qingsong Shi ◽  
Gang Wang ◽  
Nan Zhang ◽  
...  

2021 ◽  
Vol 10 (1) ◽  
pp. 466-473
Author(s):  
Tiong Reng Xian ◽  
Zaini Abdul Halim ◽  
Ching Chia Leong ◽  
Tan Jiunn Gim

This study discusses hardware-software partitioning, which is useful for system-on-chip (SoC) applications. Hardware-software partitioning attempts to obtain the lowest execution time by combining a hardware processor system and a field programmable gate array on the SoC platform in embedded system applications. A three-level hybrid algorithm called GAGAPSO is proposed in this study. The algorithm consists of two successive genetic algorithms (GAs) and one particle swarm optimization (PSO). The drawbacks of these two algorithms are GA has low convergence speed and PSO has premature convergence because of low diversity. These algorithms are combined in this study to achieve high-capacity global convergence and enhanced search efficiency. In this study, three algorithms are developed, namely, GA, GAPSO and GAGAPSO using MATLAB. These algorithms are evaluated on the basis of the number of nodes and the minimum cost that can be achieved. The number of nodes varies from 10 to 1000 nodes. The minimum cost and the number of iterations to achieve the minimum cost are recorded. Results show that GAGAPSO can converge faster than GA and GAPSO. Furthermore, GAGAPSO can achieve the lowest cost for all nodes. 


Sign in / Sign up

Export Citation Format

Share Document