Xilinx, Inc.

Yaman Umuroglu

Michaela Blott

Kornelis Antonius Vissers

Kristof Denolf

Nicholas Fraser

An example method of training a neural network includes 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.

Learning neural networks of programmable device blocks directly with backpropagation

In an example, a circuit of a neural network implemented in an integrated circuit (IC) includes a layer of hardware neurons, the layer including a plurality of inputs, a plurality of outputs, a plurality of weights, and a plurality of threshold values, each of the hardware neurons including: a logic circuit having inputs that receive first logic signals from at least a portion of the plurality of inputs and outputs that supply second logic signals corresponding to an exclusive NOR (XNOR) of the first logic signals and at least a portion of the plurality of weights; a counter circuit having inputs that receive the second logic signals and an output that supplies a count signal indicative of the number of the second logic signals having a predefined logic state; and a compare circuit having an input that receives the count signal and an output that supplies a logic signal having a logic state indicative of a comparison between the count signal and a threshold value of the plurality of threshold values; wherein the logic signal output by the compare circuit of each of the hardware neurons is provided as a respective one of the plurality of outputs.

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Dublin, Ireland

Yaman Umuroglu