跳到主要內容

臺灣博碩士論文加值系統

(3.95.131.146) 您好!臺灣時間:2021/07/25 15:04
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:楊政儒
研究生(外文):Yang, Jheng-Ru
論文名稱:Power Monitor of Embedded System with Power Signal Compression
論文名稱(外文):嵌入式系統之功率監視系統與功率訊號壓縮
指導教授:劉靖家
指導教授(外文):Liou, Jing-Jia
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:64
中文關鍵詞:嵌入式系統功率量測壓縮
相關次數:
  • 被引用被引用:0
  • 點閱點閱:145
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
即時的功率消耗對於嵌入式系統是重要的資訊,我們可以利用這些資訊最佳
化系統以延長系統使用時間和提高效能。我們使用耗電量測(Power Measurement)
為基礎,直接量測系統運作時的實際功率消耗,也讓軟體得知系統在運行時的實
際的功率消耗,以做耗電最佳化設計。因此,我們採用內嵌式系統即時量測平台
[1]做為功率量測平台並做一些改善。然而,軟體時間和功率量測系統時間是不
同步的,所以當驅動程式發一個命令給功率量測系統,而量測系統接收到這個命
令時間是不一樣的,所以軟體收集的功率消耗資訊是不正確的。因此,我們提出
一個同步化模型去補償驅動程式到功率量測系統的延遲時間。我們的實驗顯示這
段延遲是常數。
除此之外,並不是所有的功率消耗資訊都是有用的,我們只需要觀察功率訊
號的特性,像是某個時間點有比較大的功率消耗或是平均功率,所以我們應用壓
縮感測的壓縮方法來壓縮功率資料。然而,壓縮感測只適用於訊號具有稀疏特性
的訊號,但是並非所有功率訊號都是稀疏訊號。此外,壓縮感測在軟體端必須做
離散餘弦轉換來重建訊號,造成系統整體效能降低。因此,我們提出基於樣板的
壓縮方法來取代壓縮感測的不足。實驗結果顯示,大部份例子採用基於樣板壓縮
方法的壓縮比都比壓縮感測的壓縮比好。最後結合綜合上述兩種方法來壓縮功率
訊號。我們量測處理器並執行 6 種不同程式。實驗結果顯示,給定相對誤差在
15%以下,有 6 個例子壓縮比是小於 0.1,給定相對誤差在 10%以下,有 5 個例
子壓縮比是小於 0.2。而量測記憶體並執行 5 種不同程式。實驗結果顯示,給定
相對誤差在 15%以下,有 5 個例子壓縮比是小於 0.3,給定相對誤差在 10%以下,
有 5 個例子壓縮比是小於 0.4。最後,我們比較壓縮感測和基於樣板壓縮的軟體
效能。實驗結果顯示,樣板壓縮的軟體效能比壓縮感測軟體效能要好。

Real-time Power Consumption in an embedded system is an important reference information
that to optimize endurance and real-time performance. We use measurement-based power profiler
to directly measure the actual power consumption, and also allows software developers to know
the actual power consumption when their programs are executed. So that more effective power
optimization can be done. So I adopt the real-time power the measurements of an embedded
system [1] as our power measurement platform and make some improvements. First, we add
API to let software can start/stop the function to measure power. However, the software time and
power measurement system time are not synchronized, so that the time when power measurement
measure power is not exactly the time when software issues the start command. So we proposed a
synchronous model in which latency between driver to power measurement system is compensated.
Our experiments show that the latency is constant between driver to power measurement system.
Furthermore, because not all power consumption information is meaningful for us and only
certain characteristic are useful. We apply the compressive sensing (CS) compression to compress
power data. However, CS work well to sparse signals. But not all power signals are sparse.
Besides, CS have to do IDCT which result in software overhead. Therefore, we proposed template-
based compression to replace CS. The experimental results show that in most cases template-based
compression ratio (CR) better than CS compression ratio. Finally, we combine CS with template-
based compression to compress power signals and use power measurement for cpu executing 6
benchmarks. The experimental results show that in 6 cases CR is not above 0.1 when acceptable
relative error rate is 15%. In 5 cases CR is not above 0.2 acceptable relative error rate is 10%.
We use power measurement for memory executing 5 benchmarks. The experimental results show
that in 5 cases CR is not above 0.3 when acceptable relative error rate is 15%. In 5 cases CR is
not above 0.4 acceptable relative error rate is 10%. Finally, we compared CS with template-based
1software overhead. The experimental results show that template-based software overhead less than
CS.

1 Introduction 10
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.2 Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Background and Related Works 13
2.1 Real-Time Power Measurements in Embedded Systems . . . . . . . . . . . . . . . 13
2.1.1 Architecture of Real-time Power Measurements for Embedded System . . 15
2.1.2 Previous Power Measurement Board (PMBV2) . . . . . . . . . . . . . . . 17
2.2 Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.1 Sparse Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.2 Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.3 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3 Synchronization for Power Measurement System 26
3.1 Architecture of Power Measurement System . . . . . . . . . . . . . . . . . . . . . 27
3.2 Power Measurement System Flow . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2.1 Software Support for API write() . . . . . . . . . . . . . . . . . . . . . . 28
33.2.2 Software Support for API mmap() . . . . . . . . . . . . . . . . . . . . . . 29
3.3 Calibrated Driver and Hardware Synchronization . . . . . . . . . . . . . . . . . . 30
3.3.1 Calibrated Driver and Hardware Synchronization Model . . . . . . . . . . 31
3.3.2 Calibrated Driver and Hardware Synchronization Experiment . . . . . . . 31
3.4 Demonstration of Real-time Power Measurement System . . . . . . . . . . . . . . 32
4 Power Signal Compression 36
4.1 Power Signal Compression Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2 Architecture of power measurement system with compression . . . . . . . . . . . 38
4.3 CS-based Compression for Power Signal . . . . . . . . . . . . . . . . . . . . . . . 39
4.4 Template-Based Compression for Power Signal . . . . . . . . . . . . . . . . . . . 41
4.5 Simulation Results and Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.5.1 Metrics of Performance and Environment Setup . . . . . . . . . . . . . . . 46
4.5.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.5.3 Memory Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.5.5 Compression Ratio (CR) Comparison . . . . . . . . . . . . . . . . . . . . 54
4.5.6 Software Overhead Comparison . . . . . . . . . . . . . . . . . . . . . . . 56
5 Conclusions and Future Work 61
5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

[1] C.-C. Hs, “Real-Time Power Measurements of an Embedded System,” Master’s thesis, Na-
tional Tsing-Hua University, Electrical Engineering Department, 2011.
[2] D. Brooks, V. Tiwari, and M. Martonosi, “Wattch: a framework for architectural-level power
analysis and optimizations,” in Computer Architecture, 2000. Proceedings of the 27th Inter-
national Symposium, June 2000, pp. 83–94.
[3] Y.-L.-B. CHEN and I.-J. HUANG, “A real-time power analysis platform for power-aware em-
bedded system developments,” Master’s thesis, National Sun Yat-sen University, Department
of Computer Science and Engineering, 2009.
[4] D.Donoho, “Compressed sensing,” in IEEE Trans. Information Theory, vol. 52, no. 4, 2006,
pp. 1289–1306.
[5] R.Baraniuk, “Compressive sensing[lecture notes],” in IEEE Signal Processing Magazine,
vol. 24, no. 4, July 2007, pp. 118–121.
[6] V. Stankovic, L. Stankovic, and S. Cheng, “Compressive video sampling,” in Proc. EU-
SIPCO, Lausanne, Switzerland, Aug. 2008.
[7] S. Yang and M. Gerla, “Energy-Effcient Accelerometer DataTransfer for Human Body Move-
ment Studies,” in IEEE SUTC, July 2010, pp. 304–311.
[8] Atmel, AT91CAP9A-DK Development Kit User Guide,
http://www.atmel.com/dyn/resources/prod documents/doc8506.pdf, 2007,
http://www.atmel.com/dyn/resources/prod documents/doc8506.pdf.
[9] , ADS805, http://www.ti.com/lit/gpn/ADS805, 2002, http://www.ti.com/lit/gpn/ADS805.
63[10] E. Cand’es, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruc-
tion from highly in-complete frequency information,” in IEEE Trans. Information Theory,
vol. 52, Feb. 2006, pp. 489–509.
[11] S. A. Khayam, “The Discrete Cosine Transform (DCT): Theory and Application,” in ECE
802 602: Information Theory and Coding, Mar. 2003.
[12] E. Cand’es and T. Tao, “Near optimal signal recovery from randomprojections:Universal
encoding strategies?” in IEEE Trans. Information Theory, vol. 52, no. 12, Dec. 2006, pp.
5406–5425.
[13] J. Tropp and A. Gilbert, “Signal Recovery From Random Measurements Via Orthogonal
Matching Pursuit,” in IEEE Trans. Information Theory, vol. 53, no. 12, Dec. 2007, pp. 4655–
4666.
[14] M. Guthaus, J. Ringenberg, D. Ernst, T. Austin, T. Mudge, and R. Brown, “Mibench: A
free, commercially representative embedded benchmark suite,” Workload Characterization,
Annual IEEE International Workshop, vol. 0, pp. 3–14, 2001.

連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top