PPT-Cache Optimization for Real Time MPEG-4

ENCODER EEL 6935 Embedded Systems Long Presentation 2 Group Member Qin Chen Xiang Mao ECEUFL 422010 1 Outline Design goals and challenges Video encoding basics Memorycache optimization. Here we focus on cache improvements to support at least 1 instruction fetch and at least 1 data access per cycle. With a superscalar, we might need to accommodate more than 1 per cycle. Typical server and . Applications. Komal Kasat. Gaurav Chitroda. Nalini Kumar. Outline . Introduction. MPEG-4. Architecture. Simulation. Results . Conclusion. INTRODUCTION. Multimedia. Combination of graphics, video, audio. Motion Pictures Expert Group(MPEG). The coders associated with audio compression part of MPEG standard are called MPEG audio compressor.. MPEG coders use Perceptual coding. MPEG Perceptual coder. The audio input signal is sampled and quantized using PCM. Technique MR MP HT Complexity. Larger Block Size + – . 0. Higher Associativity + – 1. Victim Caches + 2. Pseudo-Associative Caches + 2. HW Prefetching of Instr/Data + 2. Compiler Controlled Prefetching + . of . Safety-Critical . RT Software . to Multicore. Marco . Caccamo. University of Illinois. at Urbana-Champaign. Outline. Motivation. Memory. -centric scheduling . theory. Background: . PRedictable. Execution Model (PREM). ECE . 7502 Class Discussion . Qing Qin. April 2, 2015. Requirements. Specification. Architecture. Logic / Circuits. Physical Design. Fabrication. Manufacturing Test. Packaging Test. PCB Test. System Test. Kepler. Zehuan Wang. zehuan@nvidia.com. Fundamental Optimization. Optimization Overview. GPU . a. rchitecture. Kernel optimization. Memory optimization. Latency optimization. Instruction . optimization. Adam . Dille. CS525 – Spring 2010. MPEG-4. Collection of compression standards. Currently 27 standards or “Parts” in total. Most widely known:. MPEG-4 Part 2 (. DivX. , . Xvid. , Nero Digital, QT6). Technique MR MP HT Complexity. Larger Block Size + – . 0. Higher Associativity + – 1. Victim Caches + 2. Pseudo-Associative Caches + 2. HW Prefetching of Instr/Data + 2. Compiler Controlled Prefetching + . Administrative Issues (04/19/2016). . Draft of Final Report is due on . Thursday, April 21, 2016. (extended to April 26). Submit a narrative semi-final report containing figures, tables, graphs and references . Kadin Tseng. Boston University. Scientific Computing and Visualization. Outline. Introduction. Timing. Example Code. Profiling. Cache. Tuning. Parallel Performance. Code Tuning and Optimization. 2. Introduction. Why it works: The principle of locality. How it . works. : The architectural details. Von Neumann Architecture. Cost/performance analysis is a constant theme in computer engineering ‒ which is why the proper choice of performance metric is important. Negin Sobhani . 1,2. , . Davide. Del Vento. 2. ,David Gill. 2. , Sam Elliot. 3,2. ,and . Srinath. Vadlamani. 4. 1. 1 . University of Iowa . 2. National Center for atmospheric Research(NCAR) . 3. Daniel Duicu and Isabela Zuzu. 26-November-2018. What we do? . 2. Demand. Resources. Rules. Planning. Optimization. Execution. Improved and . predictable results at . lower . costs. Solutions. 3. Vehicle.