Systems, methods, and media for tuning solid-state drives

A method for tuning a solid-state drive (SSD), comprising:

• (a) providing as an input to a first neural network current parameter settings of the SSD;
• (b) receiving as an output from the first neural network at least one adjustment to the current parameter settings;
• (c) based on the at least one adjustment, adjusting the current parameter settings of the SSD so that the SSD is using adjusted parameter settings;
• (d) causing the SSD to execute a workload using the adjusted parameter settings;
• (e) determining performance data of the SSD while executing the workload;
• (f) determining a reward value based on the performance data;
• and
• (g) back propagating the first neural network based on the reward value;
• (h) providing as an input to a second neural network next parameter settings of the SSD, wherein the next parameter settings are determined based on the current parameter settings and the at least one adjustment;
• and
• (i) determining an error optimization value based on the reward value and outputs of the first neural network and the second neural network,
• wherein the back propagation is based on the error optimization value.

A non-transitory computer-readable medium containing computer executable instructions that, when executed by a processor, cause the processor to perform a method for tuning a solid-state drive (SSD), the method comprising:

• (a) providing as an input to a first neural network current parameter settings of the SSD;
• (b) receiving as an output from the first neural network at least one adjustment to the current parameter settings;
• (c) based on the at least one adjustment, adjusting the current parameter settings of the SSD so that the SSD is using adjusted parameter settings;
• (d) causing the SSD to execute a workload using the adjusted parameter settings;
• (e) determining performance data of the SSD while executing the workload;
• (f) determining a reward value based on the performance data;
• and
• (g) back propagating the first neural network based on the reward value;
• (h) providing as an input to a second neural network next parameter settings of the SSD, wherein the next parameter settings are determined based on the current parameter settings and the at least one adjustment;
• and
• (i) determining an error optimization value based on the reward value and outputs of the first neural network and the second neural network,
• wherein the back propagation is based on the error optimization value.