Throughput-constrained voltage and frequency scaling for real-time heterogeneous multiprocessors

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.