Performance and Energy Bounds for Multimedia Applications on

Dual-processor Power-aware SoC Platforms

Weng-Fai WONG黄荣辉

Dept. of Computer ScienceNational University of SingaporeJoint work in collaboration with

Zhu Yongxin, Samarjit Chakraborty

Background• SoC platforms become more complicated than

classic embedded systems by carrying out multiple tasks:– to record music received by software radio– to play games while downloading another one– to talk over GPRS/3G mobile phone which stays

online checking emails– ….

• Needs to quickly explore design space of SoC for multimedia processing

• Emergence of multi-core technology

Background• Analytical approaches are necessary due to

unacceptable overheads of simulation practices to study multiple design tradeoffs

• Many efforts for performance enhancement to ensure the quality of service such as a guaranteed playback rate

• A few power-awareness efforts – dynamic voltage and frequency scaling (DVFS)– dynamic power management (DPM)

A Motivating Problem

• Under a performance constraint, how to minimize energy dissipation by trading off among:– dynamic frequency and voltage scaling

policies,– multiple frequencies of processors,– processor customization catering for

applications

Related Work• Yanhong Liu, Alexander Maxiaguine, Samarjit Chakraborty, and Wei Tsang Ooi.

Processor frequency selection in energy-aware SoC platform design for multimedia applications. RTSS 2004.

• Alexander Maxiaguine, Yongxin Zhu, Samarjit Chakraborty, and Weng-Fai Wong. Tuning soc platforms for multimedia processing: Identifying limits and tradeoffs. CODES+ISSS 2004

• L. Cai and Y.H. Lu. Energy Management Using Buffer Memory for Streaming Data, IEEE Transactions on Computer-aided Design of Integrated Circuits and Systems, 24(2):141-152, 2005

• Validation of the models against simulation results and metrics on physical processors

• Co-optimization of performance and power

Methodology• Network calculus models to identify the

upper and lower bounds using variability characterization curves

Variability Characterization Curves

• Workload curves

• Consumption curves

Variability Characterization Curves

Production curves

Service curves number of available cycles, subject to schedulers such as duty cycles

Number of activations

Power Model

• Active time – where Li is the length of activation on the i-th PE, Ωi is

the frequency of the PEi

• Leakage power – where Isubn is the sub-threshold current, Vbs is the body

bias voltage, and Ij is the reverse bias junction current

• Switching overhead – where ρi is the scheduling period of PEi, Dwakeup is the

wake-up delay, pidlep,i is the dynamic power of PEi in

the idle mode

Power Model (cont’d)

• PE’s energy

• Buffer’s energy– where Qmax

i is the maximum buffer fill level of the i-th buffer, pb

i is the i-th buffer’s dynamic power

• Total energy

Experiment Setup

• Map an MPEG-2 decoder onto PE1 and PE2

• Setting 1: parameters of Intel 80200 Xscale processor

• Setting 2: parameters based on Transmeta Crusoe processor scaled up to 70nm technology

• Buffer’s specifications are Micro SDRAM parameters

Experiment Setup (cont’d)

A Motivating Problem

• Under a performance constraint, how to minimize energy dissipation by trading off among:– dynamic frequency and voltage scaling

policies,– multiple frequencies of processors,– processor customization catering for

applications

How do scheduling policies affect the constraint?

Results on Underflow Possibilities

Underflow possibilities associated with scheduling periods (733MHz)

Results on Underflow Possibilities (cont’d)

Underflow possibilities associated with varying duty cycles (633MHz)

Which is more sensitive to schedulers, the buffer’s energy or PE’s energy?

Bounds of Buffer’s Energy

Bounds of buffer’s maximum energy associated with the same frequencies of PEs with SDRAM buffers under varying duty cycles

Bounds of Total EnergyBounds of maximum total energy associated with the same frequencies of PEs with SDRAM buffers under varying duty cycles

A Motivating Problem

• Under a performance constraint, how to minimize energy dissipation by trading off among:– dynamic frequency and voltage scaling

policies,– multiple frequencies of processors,– processor customization catering for

applications

How to reduce energy by choosing frequencies without

undermining the quality of service?

Choosing Frequencies along the Boundary

Bounds of maximum total energy associated with the combinations of frequencies of PEs with SDRAM buffers a duty cycle of 0.9

Choosing Frequencies along the Boundary (cont’d)

• Noting the surface almost monotonously increases with the frequencies except for the starting point

• Choosing frequency combinations along the boundary of the area can minimize energy without violating the performance constraint

A Motivating Problem

• Under a performance constraint, how to minimize energy dissipation by trading off among:– dynamic frequency and voltage scaling

policies,– multiple frequencies of processors,– processor customization catering for

applications

What to trade off if the frequencies are fixed?

Shifting of the Best Duty CycleBounds of maximum total energy associated with the combinations of frequencies of PEs with data cache buffers varying duty cycles

Summary

• An analytical framework based on VCC to identify both performance and energy bounds

• Studied the impacts of scheduler policies

• Explored the tradeoffs of frequencies

• Explored processor customizations

Next Steps

• Include more hardware details– Hierarchical cache systems– Communication mechanisms such as buses

• Co-optimization algorithms

• Detailed validations of the model

EASEL: Engineering Architectures and Software for the Embedded Landscape

Thank You!