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.