Workload Uncertainty Characterization and Adaptive Frequency Scaling for Energy Minimization of Embedded Systems
Anup Das1,2,a,b, Akash Kumar2,f, Bharadwaj Veeravalli2,g, Rishad Shafik1,c, Geoff Merrett1,d and Bashir Al-Hashimi1,e
1School of ECS, University of Southampton, UK.
2Department of ECE, National University of Singapore, Singapore.
A primary design optimization objective for multicore
embedded systems is to minimize the energy consumption
of applications while satisfying their performance requirement.
A system-level approach to this problem is to scale the frequency
of the processing cores based on the readings obtained from the
hardware performance monitors. However, performance monitor
readings contain uncertainty, which becomes prominent when
applications are executed in a multicore environment. This uncertainty
can be attributed to factors such as cache contention and
DRAM access time, that are very difficult to predict dynamically.
We demonstrate that such uncertainty can be controlled to make
better decision on the processor frequency in order to minimize
energy consumption. To achieve this, we propose a multinomial
logistic regression model, which combines probabilistic interpretation
with maximum likelihood (ML) estimation to classify
an incoming workload, at run-time, into a finite set of classes.
Every workload class corresponds to a frequency pre-determined
using an appropriate training set and results in minimum energy
consumption. The classifier incorporates (1) uncertainty with
arbitrary probability distribution to estimate the actual frame
workload; and (2) the frequency switching overhead, neither of
which are considered in any of the existing approaches. The
classified frequency is applied on the processing cores to execute
the workload. The proposed approach is engineered into an
embedded multicore system and is validated with a set of standard
multimedia applications. Results demonstrate that the proposed
approach minimizes energy consumption by an average 20% as
compared to the existing techniques.
Full Text (PDF)