Hardware Memory Management for Future Mobile Hybrid Memory Systems
Not on the map. Why?
Abstract (arXiv)
The current mobile applications have rapidly growing memory footprints, posing a great challenge for memory system design. Insufficient DRAM main memory will incur frequent data swaps between memory and storage, a process that hurts performance, consumes energy and deteriorates the write endurance of typical flash storage devices. Alternately, a larger DRAM has higher leakage power and drains the battery faster. Further, DRAM scaling trends make further growth of DRAMin the mobile space prohibitive due to cost. Emerging non-volatile memory (NVM) has the potential to alleviate these issues due to its higher capacity per cost than DRAM and mini-mal static power. Recently, a wide spectrum of NVM technologies, including phase-change memories (PCM), memristor, and 3D XPoint have emerged. Despite the mentioned advantages, NVM has longer access latency compared to DRAMand NVM writes can incur higher latencies and wear costs. Therefore integration of these new memory technologies in the memory hierarchy requires a fundamental rearchitect-ing of traditional system designs. In this work, we propose a hardware-accelerated memory manager (HMMU) that addresses both types of memory in a flat space address space. We design a set of data placement and data migration policies within this memory manager, such that we may exploit the advantages of each memory technology. By augmenting the system with this HMMU, we reduce the overall memory latency while also reducing writes to the NVM. Experimental results show that our design achieves a 39% reduction in energy consumption with only a 12% performance degradation versus an all-DRAM baseline that is likely untenable in the future.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Apr 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 2; 2024: 2.
4 papers.
- 2023Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacycited by 8
- 2024HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoningcited by 3
- 2024EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaborationcited by 1
- 2023Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensingcited by 0
- 2024EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaboration20 Mar 2024
- 2024HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning9 Mar 2024
- 2023Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing19 Dec 2023
- 2023Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy7 Sep 2023