Binarisation is not for backpropagation which requires a gradient (binary is a cliff) for the calculus backprop to work - this gradient is provided by the slope

Binarisation is not for backpropagation which requires a gradient (binary is a cliff) for the calculus backprop to work - this gradient is provided by the slope of the sigmoid. The OP is only suggesting binarisation for the feedforward use of nets, not the learning and training phase. from the OP : " Real valued gradients are required for SGD to work. The weights are stored in real valued accumulators and are binarized in each iteration for forward propagation and gradient computations. "