Abstract: An example method of implementing a quantized neural network (QNN) for a programmable device includes: identifying multiply-accumulate operations of neurons in the QNN; converting the multiply-accumulate operations to memory lookup operations; and implementing the memory lookup operations using a pre-compute circuit for the programmable device, the pre-compute circuit storing a pre-computed output of a neuron in the QNN for each of the memory lookup operations.

Abstract: Disclosed approaches for circuitry that implements a softmax function include difference calculation circuitry configured to calculate differences between combinations of elements, zk?zj, of a vector. First lookup circuitry is configured to lookup and output representations of exponential values, ezk?zj associated with the differences in response to input of the differences. Each adder circuit of N adder circuits sums a subset of the exponential values output from the first lookup circuitry and a value of 1. The sum output by each adder circuit denotes a denominator of a plurality of denominators of the softmax function. Second lookup circuitry is configured with quotients and looks-up and outputs quotients associated with the plurality of denominators as results of the softmax function.