Real-Time Loop Scheduling with Leakage Energy Minimization for Embedded VLIW DSP Processors

2007 ◽  
Author(s):  
Meng Wang ◽  
Zili Shao ◽  
Chun Jason Xue ◽  
Edwin H.-M. Sha
Keyword(s):  
Real Time ◽  
Energy Minimization ◽  
Loop Scheduling ◽  
Vliw Dsp ◽  
Dsp Processors ◽  
Time Loop

Compiler-assisted leakage-aware loop scheduling for embedded VLIW DSP processors

2010 ◽  
Vol 83 (5) ◽  
pp. 772-785 ◽  
Author(s):  
Meng Wang ◽  
Yi Wang ◽  
Duo Liu ◽  
Zhiwei Qin ◽  
Zili Shao
Keyword(s):  
Loop Scheduling ◽  
Vliw Dsp ◽  
Dsp Processors

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications

2016 ◽  
Vol 24 (9) ◽  
pp. 993-1002 ◽  
Author(s):  
Danilo Pani ◽  
Gianluca Barabino ◽  
Luca Citi ◽  
Paolo Meloni ◽  
Stanisa Raspopovic ◽  
...  
Keyword(s):  
Real Time ◽  
Neural Signals ◽  
Dsp Processors

Thermal-Aware Task Scheduling for Energy Minimization in Heterogeneous Real-Time MPSoC Systems

2016 ◽  
Vol 35 (8) ◽  
pp. 1269-1282 ◽  
Author(s):  
Junlong Zhou ◽  
Tongquan Wei ◽  
Mingsong Chen ◽  
Jianming Yan ◽  
Xiaobo Sharon Hu ◽  
...  
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Energy Minimization
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