Intelligent wear leveling with reduced write-amplification for data storage devices configured on autonomous vehicles

1. A data storage device, comprising:

one or more storage media components;

a controller configured to store data into and retrieve data from the one or more storage media components according to commands received in the data storage device;

an address map configured to map between:

logical addresses specified in the commands received in the data storage device, and

physical addresses of memory cells in the one or more storage media components; and

an artificial neural network configured to receive, as input and as a function of time, operating parameters indicative of a data access pattern, and generate, based on the input, a prediction to determine an optimized operation for wear leveling in the one or more storage media components;

wherein the controller is configured to perform the optimized operation for wear leveling based on the prediction;

wherein the prediction includes a write frequency indicator of data stored in a candidate memory region for wear leveling;

wherein when wear leveling is applied to the candidate memory region, data in the candidate memory region is copied to an alternative memory region;

wherein the alternative memory region is selected to have wearing higher than the candidate memory region;

wherein the alternative memory region is selected based on the write frequency indicator of data stored in the candidate memory region for wear leveling; and

wherein the controller is configured to postpone copying data from the candidate memory region to the alternative memory region when the write frequency indicator indicates that data stored in the candidate memory region is to be modified within a predetermined period of time.

2. The data storage device of claim 1 , wherein the artificial neural network includes a spiking neural network.

3. The data storage device of claim 2 , wherein the artificial neural network is configured to receive write activities in the commands and identify logical address groups having write operations correlated in time.

4. The data storage device of claim 3 , wherein the optimized operation for wear leveling includes combining wear leveling operations with predicted write operations.

5. The data storage device of claim 1 , wherein, in response to receiving a write command predicted by the artificial neural network for the data stored in the candidate memory region, the controller is configured to write data into the alternative memory region, in accordance with the write command and the data stored in the candidate memory region.

6. The data storage device of claim 5 , wherein the data storage device is configured in a vehicle; and the operating parameters include operating parameters of the vehicle.

7. The data storage device of claim 6 , wherein the operating parameters of the vehicle include a speed of the vehicle, a location of the vehicle, an input from a sensor configured on the vehicle, a status of a vehicle control, a status of an infotainment system of the vehicle, or a status of an advanced driver assistance system of the vehicle, or any combination thereof.

8. The data storage device of claim 5 , further comprising:

a neural network accelerator configured to generate the prediction using model data of the artificial neural network stored in the data storage device.

9. The data storage device of claim 8 , wherein the neural network accelerator is further configured to train the artificial neural network using training data generated in a training period; and wherein the training data includes write activities in commands received in the training period, the operating parameters in the training period, data placement schemes implemented in the address map in the training period, and write amplification performance indicators measured during the training period for the data placement schemes.

10. The data storage device of claim 8 , wherein the neural network accelerator is further configured to train the artificial neural network using training data; and wherein the training data includes the operating parameters in a time period, and optimized data placement schemes determined for data access patterns recognized from the operating parameters in the time period and optimized for a combined target of write-amplification reduction and wear-leveling.

11. A method, comprising:

storing data into and retrieving data from one or more storage media components of a data storage device according to commands received in the data storage device;

mapping, using an address map configured in the data storage device, between:

logical addresses specified in the commands received in the data storage device, and

physical addresses of memory cells in the one or more storage media components;

providing, as input, operating parameters indicative data access patterns in the data storage device as a function of time to an artificial neural network;

generating, using the artificial neural network, a prediction based on the input to determine an optimized operation for wear leveling in the one or more storage media components; and

performing, by a controller of the data storage device, the optimized operation for wear leveling based on the prediction, wherein the prediction includes a write frequency indicator of data stored in a candidate memory region for wear leveling, wherein when wear leveling is applied to the candidate memory region, data in the candidate memory region is copied to an alternative memory region, and wherein the controller is configured to postpone copying data from the candidate memory region to the alternative memory region when the write frequency indicator indicates that data stored in the candidate memory region is to be modified within a predetermined period of time.

12. The method of claim 11 , wherein the artificial neural network includes a spiking neural network; and the method further comprises:

training, in the data storage device, the artificial neural network using a training dataset generated in the data storage device.

13. The method of claim 12 , wherein the prediction includes an optimized data placement scheme optimized for a combined goal of write-amplification reduction and wear-leveling.

14. A vehicle, comprising:

a computer system configured to generate operating parameters of the vehicle as a function of time; and

a data storage device configured to identify operating parameters of the data storage device, wherein the operating parameters of the vehicle and the operating parameters of the data storage device are indicative of data access patterns in the data storage device;

wherein the data storage device includes an address map configured to map between:

logical addresses specified in commands received from the computer system, and

physical addresses of memory cells in the data storage device; and

wherein the data storage device is configured to generate, based on the operating parameters of the vehicle and the operating parameters of the data storage device as input to an artificial neural network, a prediction to determine an optimized operation for wear leveling among memory cells in the data storage device; and

wherein the data storage device is configured to perform the optimized operation for wear leveling based on the prediction, wherein the prediction includes a write frequency indicator of data stored in a candidate memory region for wear leveling, wherein when wear leveling is applied to the candidate memory region, data in the candidate memory region is copied to an alternative memory region, and wherein the data storage device is configured to postpone copying data from the candidate memory region to the alternative memory region when the write frequency indicator indicates that data stored in the candidate memory region is to be modified within a predetermined period of time.

15. The vehicle of claim 14 , wherein the artificial neural network includes a spiking neural network; and the data storage device is further configured to generate training data and train the spiking neural network to generate the prediction.