Reducing energy consumption of multiprocessor SoC architectures by exploiting memory bank locality

The next generation embedded architectures are expected to accommodate multiple processors on the same chip. While this makes interprocessor communication less costly as compared to traditional high-end parallel machines, it also makes off-chip requests very costly. In particular, frequent off-chip memory accesses do not only increase execution cycles but also increase overall power consumption. One way of alleviating this power problem is to divide the off-chip memory into multiple banks, each of which can be power-controlled independently using low-power operating modes. In this article, we focus on a multiprocessor-system-on-a-chip (MPSoC) architecture with a banked memory system, and show how code and data optimizations can help us reduce memory energy consumption for embedded applications with regular data access patterns, for example, those from the embedded image and video processing domain. This is achieved by ensuring bank locality, which means that each processor localizes its accesses into a small set of banks in a given time period. We present a mathematical formulation of the bank locality problem. Our formulation is based on constructing a set of matrix equations that capture the mappings between the data, computation, processor, and memory bank spaces. Based on this formulation, we propose a heuristic solution to the bank locality problem under different scenarios. Our solution involves an iterative process through which we try to satisfy as many matrix constraints as possible; the unsatisfied constraints represent the degree of degradation in bank locality. Finally, we report extensive experimental results showing the effectiveness of our strategy in practice. Our results show that the proposed solution improves bank locality significantly, and reduces the overall memory system energy consumption by up to 34% over an approach that makes use of the low-power modes but does not employ our strategy.