There have been numerous efforts on Scratch-Pad Memory (SPM) management in the context of single CPU systems and, more recently, multi-processor architectures. This paper presents a novel SPM space utilization strategy, for embedded chip multi-processor systems, based on recomputing the value of an off-chip data element using on-chip (SPM resident) data elements. In doing so, our goal is to eliminate the corresponding off-chip memory access that would otherwise be performed, and save execution cycles and power. This paper presents the details of a compiler algorithm that implements this approach and reports the experimental data we collected using six data-intensive applications. Our results indicate that, on a four processor chip multiprocessor, the average performance improvement our approach brings is about 11.8%, over a state-of-the-art SPM management scheme. We also observed that there is a specific range of total SPM size/total data size ratios, for which our approach generates the best results. Finally, our results also show that the proposed approach brings consistent improvements when the number of CPUs is varied between 2 and 16.

Banking has been identified as one of the effective methods using which memory energy can be reduced. We propose a novel approach that improves the energy effectiveness of a banked memory architecture by performing extra computations if doing so makes it unnecessary to reactivate a bank which is in the low-power operating mode. More specifically, when an access to a bank, which is in the low-power mode, is to be made, our approach first checks whether the data required from that bank can be recomputed by using the data that are currently stored in already active banks. If this is the case, we do not turn on the bank in question, and instead, recalculate the value of the requested data using the values of the data stored in the active banks. Given the fact that the contribution of the leakage consumption to overall energy budget keeps increasing, the proposed approach has the potential of being even more attractive in the future. Our experimental results collected so far clearly show that this recomputation based approach can reduce energy consumption significantly.