Abstract: A processing system identifies and removes stuck channels in a quantized neural network (QNN), where a stuck channel is one whose outputs are always mapped to the same quantized number. The processing system identifies, at a layer of the neural network, a first channel as a stuck channel based on the first channel having a constant output. In response to identifying the first channel as a stuck channel, the processing system adjusts a first operator of the layer.

Abstract: Embodiments herein describe pruning of technology-mapped machine learning-related circuits at bit-level granularity, including techniques to efficiently remove look-up tables (LUTs) of a technology-mapped netlist while maintaining a baseline accuracy of an underlying machine learning model. In an embodiment, a LUT output of a current circuit design is replaced with a constant value, and at least the LUT and LUTs within a maximum fanout-free cone (MFFC) are removed, to provide an optimized circuit design. The current circuit design or the optimized circuit design is selected as a solution based on corresponding training data-based accuracies and metrics (e.g., LUT utilization), and optimization criteria. If the optimized circuit design is rejected, inputs to the LUT may be evaluated for pruning. A set of solutions may be evaluated based on validation data-based accuracies and metrics of the corresponding circuit design. Solutions that do not meet a baseline accuracy may be discarded.

Abstract: 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.

Abstract: 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.

Abstract: 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

Abstract: 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