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“Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems”, IEEE Micro 2012.

“Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems”, IEEE Micro 2012. Jinwook Oh ; Gyeonghoon Kim ; Injoon Hong ; Junyoung Park ; Seungjin Lee ; Joo -Young Kim ; Jeong -Ho Woo ; Hoi-Jun Yoo. Presenter: Juseong Lee, 2013021037. Outline. Introduction

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“Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems”, IEEE Micro 2012.

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  1. “Low-Power, Real-Time Object-Recognition Processors for Mobile Vision Systems”, IEEE Micro 2012. Jinwook Oh ; Gyeonghoon Kim ; Injoon Hong ; Junyoung Park ; Seungjin Lee ; Joo-Young Kim ; Jeong-Ho Woo ; Hoi-Jun Yoo Presenter: Juseong Lee, 2013021037

  2. Outline • Introduction • Background • Main Idea • Implementation • Conclusion • Evaluation Object Recognition by Juseong Lee

  3. Outline • Introduction • Background • Main Idea • Implementation • Conclusion • Evaluation Object Recognition by Juseong Lee

  4. Introduction Source by MBN News

  5. Introduction • Object recognition system • Require real-time operation • High performance • Low power in mobile system • How can implement? • Find suitable algorithm • SIFT algorithm • Hardware optimization • Algorithm optimization • Make exclusive processor • Parallel computation • Multi-threading • NoC Source by VOLVO SIFT - Scale Invariant Feature Transform NoC- Network on Chip

  6. Outline • Introduction • Background • Main Idea • Implementation • Conclusion • Evaluation Object Recognition by Juseong Lee

  7. Background Knowledge • What is SIFT algorithm? • Scale Invariant Feature Transform • The most popular candidate • For how to extract some interest points out of the object and describe them • Robust against changes in translation, scaling, and rotation. Image matching by SIFT

  8. Background Knowledge • What’s the problem in SIFT-based object recognition? • Consumes a lot of power • Owing to the heavy computation required in descriptor Gen. and matching • Today’s high-resolution image sensors & tight power budgets • Make real-time SIFT implementation in mobile device even harder Scare resources problem

  9. Outline • Introduction • Background • Main Idea • Implementation • Conclusion • Evaluation Object Recognition by Juseong Lee

  10. Main Idea • How can we solve the problem? • Make an object-recognition processor • Using an attention-based recognition algorithm • For energy efficiency • A heterogeneous multicore architecture • For data and thread parallelism • Network-on-Chip(NoC) communication • For high bandwidth • The processor determines Regions of Interest(ROI) part of image • For minimizing unnecessary computations • Heterogeneous multicore architecture • provides several types of parallelism • achieves high throughput • low power consumption • High-bandwidth NoC plays a role as the communications backbone

  11. Why find ROI? • Image processing algorithm has no regard throughput Example) Edge detection Image size 480 x 360 172,800 computations! Objects have feature! You can select part for reducingcomputation!

  12. Main Idea – BONE V Using Conventional method Using Main Idea

  13. Main Idea – Algorithm • Attention-based object recognition

  14. Main Idea – Architecture Pixel level parallel Very long instruction word 3 stage task level pipeline 1.5x↓ power consumption 5 stage fine-grained pipeline 3.45x↑ pipeline throughput

  15. BONE-V5: SMT-enabled heterogeneous multicore processor • Throughput-optimized SFEC • Find ROI tile for energy efficiency • Memory locality with high bandwidth utilization • Latency-optimized FMP • ROI tile and NoC help latency • Power-optimized MLE • Changes the core’s thread allocation • and operating voltage and frequency dynamically SFEC:SMT-enabled Feature Extraction Cluster FMP:Feature Matching Processor MLE:Machine Learning Engine

  16. Outline • Introduction • Background • Main Idea • Implementation • Conclusion • Evaluation Object Recognition by Juseong Lee

  17. Implementation

  18. Implementation - Comparing

  19. Implementation - Comparing

  20. Outline • Introduction • Background • Main Idea • Implementation • Conclusion • Evaluation Object Recognition by Juseong Lee

  21. Conclusion • Energy efficient system is important to improve performance • Algorithm and architecture have to optimize at the same time • BONE-V multicore processors can apply real-time object recognition system • Future BONE-V processors will further lower the power consumption.

  22. Outline • Introduction • Background • Main Idea • Implementation • Conclusion • Evaluation Object Recognition by Juseong Lee

  23. Evaluation • Table 3 has to contain the result that comparing other recognition processor • When hardware optimization, Not only overallalgorithmbut particular algorithm blockoptimization are needed • CORDIC based gradientand magnitude computation

  24. Thanks for Ur listening! Thanks! Juseong_lee@korea.ac.kr

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