Learning neural networks of programmable device blocks directly with backpropagation

A non-transitory computer readable medium having stored thereon instructions that when executed by a processor cause the processor to perform a method of training a neural network, comprising:

• defining hardware building blocks (HBBs), neuron equivalents (NEQs), and conversion procedures from NEQs to HBBs;
• defining the neural network using the NEQs in a machine learning framework;
• training the neural network on a training platform;
• and
• converting the neural network as trained into a netlist of HBBs using the conversion procedures to convert the NEQs in the neural network to the HBBs of the netlist.

A computing system, comprising:

• a system memory configured to store code implementing design tools;
• a training platform;
• and
• a processor coupled to the system memory and the training platform, the processor configured to execute the code to perform training of a neural network by:
• defining hardware building blocks (HBBs), neuron equivalents (NEQs), and conversion procedures from NEQs to HBBs;
• defining the neural network using the NEQs in a machine learning framework;
• training the neural network on the training platform;
• and
• converting the neural network as trained into a netlist of HBBs using the conversion procedures to convert the NEQs in the neural network to the HBBs of the netlist.