US 2022/0188011 A1 · Flash Translation Layer Design Using Reinforcement Learning

Abstract

The subject matter described herein provides systems and techniques to counter a high write amplification in physical memory, to ensure the longevity of the physical memory, and to ensure that the physical memory wears in a more uniform manner. In this regard, aspects of this disclosure include the design of a Flash Translation Layer (FTL), which may manage logical to physical mapping of data within the physical memory. In particular, the FTL may be designed with a mapping algorithm, which uses reinforcement learning (RL) to optimize data mapping within the physical memory. The RL technique may use a Bellman equation with q-learning that may rely on a table being updated with entries that take into account at least one of a state, an action, a reward, or a policy. The RL technique may also make use a deep neural network to predict particular values of the table.

Claims20

Dependent claims

A method of writing data to physical memory, the method comprising:

determining a current state based on the physical memory;
and
if the current state is in a table comprising a plurality of entries:
executing, using one or more processors, an agent to choose a current action from a plurality of actions, wherein each of the plurality of actions is associated with a location within the physical memory to write data;
writing the data to the physical memory based on the current action;
observing a reward based at least in part on the current state and the current action;
updating, using the one or more processors, at least one entry of the plurality of entries of the table, based on the observed reward, to produce an updated table.

4 more elements

Dependencies are inferred from the claim text.

Flash Translation Layer Design Using Reinforcement Learning

Life of this patent

Flash Translation Layer Design Using Reinforcement Learning

Life of this patent

Abstract

Claims20