Static Rate-Optimal Scheduling of Iterative Data-Flow Programs Via Optimum Unfolding
IEEE Transactions on Computers
Task scheduling and voltage selection for energy minimization
Proceedings of the 39th annual Design Automation Conference
Low Power Digital CMOS Design
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Proceedings of the conference on Design, automation and test in Europe - Volume 1
FAST: Frequency-aware static timing analysis
ACM Transactions on Embedded Computing Systems (TECS)
On retiming of multirate DSP algorithms
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Vector processing as an enabler for software-defined radio in handheld devices
EURASIP Journal on Applied Signal Processing
Energy-Aware Scheduling for Streaming Applications on Chip Multiprocessors
RTSS '07 Proceedings of the 28th IEEE International Real-Time Systems Symposium
ECRTS '08 Proceedings of the 2008 Euromicro Conference on Real-Time Systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Power Minimisation for Real-Time Dataflow Applications
DSD '11 Proceedings of the 2011 14th Euromicro Conference on Digital System Design
Hi-index | 0.00 |
Voltage and Frequency Scaling (VFS) can effectively reduce energy consumption at system level. Most work in this field has focused on deadline-constrained applications with finite schedule lengths. However, in typical real-time streaming, processing is repeatedly activated by indefinitely long data streams and operations on successive data instances are overlapped to achieve a tight throughput. A particular application domain where such characteristics co-exist with stringent energy consumption constraints is baseband processing. Such behavior requires new VFS scheduling policies. This paper addresses throughput-constrained VFS problems for real-time streaming with discrete frequency levels on a heterogeneous multiprocessor. We propose scaling algorithms for two platform types: with dedicated VFS switches per processor, and with a single, global VFS switch. We formulate Local VFS using Mixed Integer Linear Programming (MILP). For the global variant, we propose a 3-stage heuristic incorporating MILP. Experiments on our modem benchmarks show that the discrete local VFS algorithm achieves energy savings close to its continuous counterpart, and local VFS is more effective than global VFS. As an example, for a WLAN receiver, running on a modem realized as a heterogeneous multiprocessor, the continuous local VFS algorithm reduces energy consumption by 29%, while the discrete local and global algorithms reduce energy by 28% and 16%, respectively, when compared to a on/off energy saving policy.