Adaptive and hierarchical convolutional neural networks using partial reconfiguration on fpga

An adaptive and hierarchical convolutional neural network (AH-CNN), comprising:

• a shallow part for extracting low-level features of input data;
• a first deep part for extracting high-level features of the input data;
• and
• a decision layer configured to:
• receive a first output from the shallow part;
• evaluate a performance of the shallow part;
• determine whether to pass the first output from the shallow part to the first deep part based on the performance of the shallow part;
• and
• when it is determined to pass the output from the shallow part to the first deep part, cause a partial reconfiguration of a processor to instantiate the first deep part.

An embedded computing device for adaptively implementing a dynamic neural network, the embedded computing device comprising:

• a memory storing data;
• and
• a first processor configured to:
• receive the data from the memory;
• implement a first neural network configured to perform a first feature extraction at a first confidence level;
• and
• when the first confidence level is below a threshold confidence level, partially reconfigure the first processor to implement a second neural network having a distinct architecture from the first neural network, the second neural network being configured to perform a second feature extraction at a second confidence level.