Virtualizing on-chip distributed ScratchPad memories for low power and trusted application execution

Abstract

Emerging multicore platforms are increasingly deploying distributed scratchpad memories to achieve lower energy and area together with higher predictability; but this requires transparent and efficient software management of these critical resources. In this paper, we introduce the concept of ScratchPad Memory virtualization, a hardware/software run-time layer (called SPMVisor) that virtualizes the scratchpad memory space in order to facilitate the use of distributed SPMs in an efficient, transparent and secure manner. We introduce the notion of virtual scratchpad memories (vSPMs), which can be dynamically created and managed as regular SPMs. The SPMVisor exploits policy-driven allocation strategies based on application privilege levels and data level prioritization metrics (e.g., utilization) to efficiently manage the on-chip memory real-estate. Our experimental results on Mediabench/CHStone benchmarks running on various Chip-Multiprocessor configurations and software stacks (RTOS, virtualization, secure execution) showed that SPMVisor enhances performance by 71 % on average and reduces power consumption by 79 % on average with respect to traditional context switching schemes. We showed the benefits of using vSPMs in a various environments (a RTOS multi-tasking environment, a virtualization environment, and a trusted execution environment). Furthermore, we explored the effects of mapping instructions and data onto vSPMs, and showed that sharing on-chip space reduces both execution time and energy by an average 16 % and 12 % respectively. We then compared our priority-driven memory allocation scheme with traditional dynamic allocation and showed an average 54 % execution time reduction and 65 % energy savings. Finally, to further validate the SPMVisor’s benefits, we modified the initial bus-based architecture to include a mesh-based CMP with up to 4×4 nodes. We were able to observe that SPMVisor’s priority-driven allocator was able to reduce execution time by an average 17 % with respect to competing allocation policies, while saving an average 65 % across various architectural configurations. We were also able to observe that SPMVisor reduces execution time by an average 12.6 % with respect to competing allocation policies, while saving an average 63.5 % in total energy for various architectural configuration running 1024 jobs.