  We present a post-training quantization algorithm with error estimates
relying on ideas originating from frame theory. Specifically, we use
first-order Sigma-Delta ($\Sigma\Delta$) quantization for finite unit-norm
tight frames to quantize weight matrices and biases in a neural network. In our
scenario, we derive an error bound between the original neural network and the
quantized neural network in terms of step size and the number of frame
elements. We also demonstrate how to leverage the redundancy of frames to
achieve a quantized neural network with higher accuracy.
