Statistical Access Interval Prediction for Tightly Coupled Memory Systems
Robert Wittig, Mattis Hasler, Emil Matúš, Gerhard Paul Fettweis · 2019
Sharing memory in embedded systems presents a promising approach to increase the area utilization of these constraint platforms. However, sharing inevitably results in access conflicts, which diminish the overall system performance. As a counter measure, we propose Access Interval Prediction. We argue that most memory transaction of embedded processors can be reliably predicted in the time domain. Therefore, preallocation of shared resources can be used to avoid collisions in the memory system. Our statistical model shows an accuracy of over 90 percent, thus significantly reducing memory contention.