FIGARO: Improving System Performance via Fine-Grained In-DRAM Data Relocation and Caching
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Abstract (arXiv)
DRAM Main memory is a performance bottleneck for many applications due to the high access latency. In-DRAM caches work to mitigate this latency by augmenting regular-latency DRAM with small-but-fast regions of DRAM that serve as a cache for the data held in the regular-latency region of DRAM. While an effective in-DRAM cache can allow a large fraction of memory requests to be served from a fast DRAM region, the latency savings are often hindered by inefficient mechanisms for relocating copies of data into and out of the fast regions. Existing in-DRAM caches have two sources of inefficiency: (1) the data relocation granularity is an entire multi-kilobyte row of DRAM; and (2) because the relocation latency increases with the physical distance between the slow and fast regions, multiple fast regions are physically interleaved among slow regions to reduce the relocation latency, resulting in increased hardware area and manufacturing complexity. We propose a new substrate, FIGARO, that uses existing shared global buffers among subarrays within a DRAM bank to provide support for in-DRAM data relocation across subarrays at the granularity of a single cache block. FIGARO has a distance-independent latency within a DRAM bank, and avoids complex modifications to DRAM. Using FIGARO, we design a fine-grained in-DRAM cache called FIGCache. The key idea of FIGCache is to cache only small, frequently-accessed portions of different DRAM rows in a designated region of DRAM. By caching only the parts of each row that are expected to be accessed in the near future, we can pack more of the frequently-accessed data into FIGCache, and can benefit from additional row hits in DRAM. Our evaluations show that FIGCache improves the average performance of a system using DDR4 DRAM by 16.3% and reduces average DRAM energy consumption by 7.8% for 8-core workloads, over a conventional system without in-DRAM caching.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Sep 2020.
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Citing papers per year: 2022: 3; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2022Machine Learning Training on a Real Processing-in-Memory Systemcited by 4
- 2022An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory Systemcited by 2
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databasescited by 1
- 2023TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systemscited by 1
- 2026From Bit-Position Sensitivity to Unequal Error Protection for DNN Inference Memorycited by 0
- 2026From Bit-Position Sensitivity to Unequal Error Protection for DNN Inference Memory21 Jul 2026
- 2023TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems3 Apr 2023
- 2022An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System16 Jul 2022
- 2022Machine Learning Training on a Real Processing-in-Memory System13 Jun 2022
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases29 May 2022