Optimization of Dynamic Data Structures in Multimedia Embedded Systems Using Evolutionary Computation

Transcription

1 Optimization of Dynamic Data Structures in Multimedia Embedded Systems Using Evolutionary Computation D. Atienza, C. Baloukas, L. Papadopoulos, C. Poucet, S. Mamagkakis, J. I. Hidalgo, F. Catthoor, D. Soudris and J. Lanchares DACYA / Complutense University of Madrid (UCM) LSI / Ecole Polytechnique Fédérale de Lausanne (EPFL) VLSI / Democritus University of Thrace (DUTH) DESICS/ Inter-University Micro-Electronics Center (IMEC)

2 Outline Multimedia Embedded Systems Evolutionary Computation Optimization Method Proposed Flow Case Studies and Results Conclusions

3 Outline Multimedia Embedded Systems Evolutionary Computation Optimization Method Proposed Flow Case Studies and Results Conclusions

4 Introduction New multimedia applications Video Scalable video rendering Complex games Wireless communications Embedded systems Conflicting set of metrics Performance Memory resources Energy

5 Introduction (2) Memory Management in Embedded Systems Multimedia Games Memory low Memory battery low low HW Emb. Systems Scalable video No memory space! No battery! Limit. Resources 63 Objective: Definition of fast optimization methods for Dynamic Data Types (DDTs) in new embedded systems

6 Current Methodologies Static Allocation High quality Medium qual. Low quality Scalable 3D decoding object5 memory Maximum size object3 object4 object3 object4 object3 object2 object2 object2 object2 object1 object1 object1 object1 object1 Worst case is not realistic (oversized)! time

7 Use of Dynamic Data Types Run-time High quality Medium qual. Low quality Scalable 3D decoding memory Maximum size object 4 object 5 object 4 object 3 object 3 object 3 object2 object2 object2 object2 object1 object1 object1 object1 object1 Memory usage scales as requested! time

8 Related Work Static data (general-purpose and embedded) Optimizations & techniques to reduce energy and power consumption, including performance trade-offs University of Dortmund, University of Bologna/Torino, IMEC, Penn State University, IRVINE, et al. Dynamic data (general-purpose) Analysis design space (e.g., basic DDTs, fragmentation) Leeman, Grunwald, Zorn et al. Optimizations for memory footprint vs performance Atienza, Mamagkakis et al. Little work on heuristics for DDTs customization in embedded systems!

9 Outline Multimedia Embedded Systems Evolutionary Computation Optimization Method Proposed Flow Case Studies and Results Conclusions

10 Evolutionary Computation Genetic algorithms Stochastic optimization heuristics Based on Darwin s theory evolution Fitting function to select best individuals Fast converging if good original individuals VEGA (Vector Evaluated Genetic Algorithm) Multi-objective optimization algorithm Characteristics: 4 basic DDTs [ICME 04] SLL/DLL, array, pointer arrays (AR(P)), roving pointers

11 Evolutionary Computation: VEGA (1) Basic evolutionary operators Shuffling Mutation Crossover Chromosome representation 0 to 1 2 to 5 6 to 8 9 to to to to to 25 Bit positions Levels Basic Fields Elements DS Levels Basic Fields Elements DS Meaning Variable 1 Variable 2

12 Evolutionary Computation: VEGA (2) Multi-Objective iterative exploration Metrics A, B and C Generation i Individual 1 Population Generation i Energy Best A Best B Best C Selection Shuffling Crossover Mutation Memory Performance Select populations Individual M Shuffling Crossover Mutation Generation i +1 Population Generation i + 1

13 Outline Multimedia Embedded Systems Evolutionary Computation Optimization Method Proposed Flow Case Studies and Results Conclusions

14 Optimization Method: Flow Profiling & multi-objective optimization Application Platform description Profiling report Multi-objective evolutionary alg. Implement. Final DDTs Analytical characterization Easy control of multi-objective optimization for the user: GUI

15 Optimization Method: GUI 3D Pareto Curve - Simblob Simulator Objectives, restrictions Target architecture Multi-Dimensional Pareto curve report /graph (power, memory footprint, performance)

16 Outline Multimedia Embedded Systems Evolutionary Computation Optimization Method Proposed Flow Case Studies and Results Conclusions

17 Case Studies & Results New applications portable embedded systems Dynamic Large difference best vs worst case Data-dominated: video rendering, games Intensive requirements of DDTs Memory use: between 70-90% of total Energy: between 45-80% of total memory subsystem 3 case studies VDrift Lilith Simblob

18 Case Studies & Results: Vdrift (2) 3D Race Simulation 25 DDTs: cars, obstacles and enemies

19 Case Studies & Results: Lilith (3) 3D Virtual Reality World Simulator 5 DDTs: Dynamic path generation and weather control

20 Case Studies & Results: Simblob (4) Video Rendering & Fluid Simulator 3 DDTs: Liquid movement, movement and solid surface generation

21 Case Studies & Results: Mem. Accesses (5) High reliability for DDT design space exploration 75-85% of optimal DDTs structures found E.g., Vdrift: 22 out of 25 Complex DDTs: DLL, SLL, AR(P),AR, SLL(O) and DLL(O)

22 Case Studies & Results: Exploration (6) Significant reduction in exploration time Optimization Vdrift Simblob Lilith (memory footprint) Exhaustive 9 days 14h 1h Depth-first Branch & bound Proposed multi-objective evolutionary optimization 21 (29% gain) 5 (19% gain) 1 (20% gain)

23 Outline Multimedia Embedded Systems Evolutionary Computation Optimization Method Proposed Flow Case Studies and Results Conclusions

24 Conclusions Application of multi-objective evolutionary optimization for DDTs in embedded systems Significant reduction in exploration time High reliability for real-life applications Future work Additional genetic algorithms to be considered Parallel implementations, combined LP-genetic algorithms Exploration of new architectural features Effect of scratchpad memories Additional cache levels

25 THANK YOU! QUESTIONS?