Dynamic deep learning processor architecture

A method comprising:

• operating deep learning processor using a first configuration;
• tracking one or more parameters of a deep learning program using the deep learning processor in the first configuration, wherein the one or more parameters comprises sparsity of data analyzed by the deep learning processor;
• and
• reconfiguring one or more processing elements of a programmable fabric of the deep learning processor to a second configuration to enhance efficiency of the deep learning processor executing the deep learning program based at least in part on the one or more parameters.

Tangible, non-transitory, and computer-readable medium having instructions stored thereon instructions, that when executed, are configured to cause a deep learning processor to:

• configure the deep learning processor in a first configuration;
• operate the deep learning processor using the first configuration;
• track one or more parameters of a deep learning program using the deep learning processor in the first configuration, wherein the one or more parameters comprises sparsity of data analyzed by the deep learning processor;
• and
• based on the one or more tracked parameters, reconfigure one or more processing elements of a programmable fabric to a second configuration to enhance efficiency of the deep learning processor executing the deep learning program.