DREAM: Enabling Low-Overhead Rowhammer Mitigation via Directed Refresh Management
Hritvik Taneja, Moinuddin K. Qureshi
Abstract
This paper focuses on Memory-Controller (MC) side Rowhammer mitigation. MC-side mitigation consists of two parts: First, a tracker to identify the aggressor rows. Second, a command to let the MC inform the DRAM chip to perform victim-refresh for the specified aggressor row. To facilitate this, prior works assumed a per-bank Nearby Row Refresh (NRR) command. However, JEDEC standards did not support NRR. Instead, JEDEC introduced Directed Refresh Management (DRFM), which can simultaneously perform mitigations for one row each in 8 (DRFMsb) or 32 (DRFMab) banks. As DRFM stalls 8-32 banks, it incurs high overheads. For example, at a threshold of 2K, PARA incurs a slowdown of 3.9% with NRR, 12.7% with DRFMsb, and 49% with DRFMab. Although counter-based trackers can avoid these slowdowns, they require significant storage. The goal of our paper is to reduce the performance and storage overheads of MC-based mitigations by using properties of DRFM.
Our paper proposes DREAM, DRFM-Aware Rowhammer Mitigation. We propose two variants of DREAM. Our first design, DREAM-R, reduces the performance overhead of randomized trackers by increasing the time between sampling the row and issuing a DRFM. The delay allows other banks the time to sample their own rows, thereby increasing the number of rows mitigated under a single DRFM. DREAM-R reduces the average performance overhead of PARA from 12.7% (DRFMsb) to 4.24% and MINT from 15.9% (DRFMsb) to 2.1%. We bound the impact of delayed DRFM on the tolerated Rowhammer threshold. Our second design DREAM-C, reduces the storage for counter-based trackers by leveraging the fact that DRFM can concurrently mitigate several rows. DREAM-C forms a gang containing 32-256 rows, randomly selected equally from all the 32 banks, and allocates a single counter for the entire gang. DREAM-C reduces the storage required at a threshold of 500 to only 1KB/bank, which is 8x lower than Graphene while avoiding the complexity of CAM lookups and incurring negligible slowdown. We also show that DREAM compares favorably to PRAC.
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