Space-efficient FTL for Mobile Storage via Tiny Neural Nets

We present RQFTL, a demand-based FTL for mobile storage controllers that boosts the effective Logical-To-Physical (L2P) address translation cache capacity over state-of-the-art techniques. RQFTL stores a large part of the L2P cache in a compressed form, and employs a learned data structure called RQRMI that leverages tiny neural nets to quickly find the correct translation entry in the cache. RQFTL uses neural network inference for cache lookups, and rapidly retrains the neural nets to efficiently handle L2P cache updates. It is specifically optimized to achieve high coverage for scattered read accesses, making it suitable for popular read-skewed workloads such as mobile gaming. We evaluate RQFTL on hours-long real-world I/O traces of popular modern mobile apps, including games, video editing, and social networking apps collected on Google Pixel 6a phone. We show that RQFTL outperforms all the state-ofthe-art FTLs in these workloads, increasing the effective L2P cache capacity by over an order of magnitude compared to DFTL and up to 5x over the recent LeaFTL. As a result, it achieves 65%, and 25% lower miss rate compared to DFTL and LeaFTL respectively, under the same SRAM capacity, and allows reduction of the total SRAM capacity of a controller by about a third of that of LeaFTL.