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Advanced SW/HW Optimization Techniques for Application Specific MCSoC

Advanced SW/HW Optimization Techniques for Application Specific MCSoC. m5151117 Yumiko Kimezawa Supervised by Prof. Ben Abderazek Graduate School of Computer Science and Engineering Adaptive Systems Laboratory. Outline. Background Problems Research Goal Research Approach

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Advanced SW/HW Optimization Techniques for Application Specific MCSoC

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  1. Research Plan Seminar Advanced SW/HW Optimization Techniques for Application Specific MCSoC m5151117 Yumiko Kimezawa Supervised by Prof. Ben Abderazek Graduate School of Computer Science and Engineering Adaptive Systems Laboratory

  2. Outline Research Plan Seminar • Background • Problems • Research Goal • Research Approach • Research Schedule

  3. Background Research Plan Seminar • Electrocardiography (ECG) • Electrical activity of the heart • Used for diagnosis of heart disease • Processing ECG signals involves heavy computation • Previous proposed ECG processing system • Parallel processing using additional cores for analyzing ECG signals

  4. BackgroundPeriod-Peaks Detection (PPD) Algorithm (1) Research Plan Seminar Figure: A typical ECG graph

  5. BackgroundPeriod-Peaks Detection (PPD) Algorithm (2) Research Plan Seminar Peaks processing Data reading Extraction Derivation Discrimination Autocorrelation Store of results Finding interval Period detection A. Ben Abdallah, Y. Haga, K. Kuroda, An Efficient Algorithm and Embedded Multicore Implementation for ECG Analysis in Multi-lead Electrocardiogram Records, IEEE Proc. of the 39th he International Conference on Parallel Processing , San Diego, pp.99-103, Sept. 13-16, 2010.

  6. BackgroundSystem Base Architecture (1) Research Plan Seminar • The system consists of mainly 2 modules • Master module • Signal reading, filtering and display part • PPD module • Analyzing ECG signal using Period-Peaks Detection (PPD) algorithm August 22, 2011 Master's Thesis Research Plan 6

  7. Research Plan Seminar BackgroundSystem Base Architecture* (2) • 3-lead system is implemented External Memory Buffer ECG Signal Analysis ADC 1 FIR 1 Patient: A P = # mV Q = # mV R = # mV S = # mV T = # mV U = # mV Interval = # ms Not implemented ADC 12 FIR12 Our ideal system architecture 12 leads * A. Ben Abdallah, Y. Haga, K. Kuroda, An Efficient Algorithm and Embedded Multicore Implementation for ECG Analysis in Multi-lead Electrocardiogram Records, IEEE Proc. of the 39th he International Conference on Parallel Processing , San Diego, pp.99-103, Sept. 13-16, 2010. 2:Filtering 3:Analysis 4:Display 1:Signal reading

  8. Problems Research Plan Seminar • BANSMOM runs sample data only • Can not read actual data • Difficultly in estimation of real processing time • Cannot estimate real system complexity and power • Low hardware usability • The more leads, the more larger logic utilization • Current driver software is not well parallelized

  9. Research Goal Research Plan Seminar • Research about software and hardware optimization techniques for Embedded Multicore SoC (BANSMOM) • Capturing and analyzing of real ECG signals • Research about HW optimization • Parallelizing PPD algorithm (driver software)

  10. Research Approach (1) Research Plan Seminar • Hardware/Software optimization • Hardware • Adding A/D converters • Fast data transfer between each memory • DMA controller • Software • Parallelizing Period-Peaks Detection (PPD) algorithm by refining the code and looking for parallel tasks

  11. Research Approach (2) Research Plan Seminar JTAG UART : Data flow : Control signal Graphic LCD LED Data conversion HSMC FPGA Graphic LCD Controller Slave CPU Slave CPU Memory DMA controller External Memory LED Controller A/D converter Avalon Bus Timer Shared Memory Master CPU Master CPU Memory FIR Filter Timer Master Module PPD Module Line-in Analog ECG data from the sensor

  12. Evaluation Methodology Research Plan Seminar • Environment • Language: Verilog HDL • Tools: Quartus II, SOPC Builder, and NIOS II IDE • Target device: Stratix III DSP Board (EP3SL150F1152C2) • Sensor: Pulse wave/PCG sensor TK-701T • Target data: actual ECG signals • Parameters • Hardware complexity • Processing time Stratix III Sensor

  13. Research Schedule Research Plan Seminar • Investigating suitable resolution and sampling rate for A/D conversion • Selecting appropriate an A/D converter • Adding the A/D converter into the system • Getting actual data using the sensor • Adding DMA controller into the system • Optimization of software • Verification of the system • Writing master’s thesis

  14. Research Plan Seminar • Thank you for listening

  15. Research Plan Seminar

  16. Research Approach (2) Research Plan Seminar • Based on autocorrelation approach Peaks detection Reading data Extraction of max point Derivation Discrimination Autocorrelation Store results Find interval Parallelizing this phase Period detection

  17. Research Schedule Research Plan Seminar • Investigating suitable resolution and sampling rate for A/D conversion • Selecting appropriate an A/D converter • Adding the A/D converter into the system • Getting actual data using the sensor • Adding DMA controller into the system • Optimization of software • Verification of the system • Writing master’s thesis

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