I'm a grad student from EE.
So, basically, there's an electrical circuit that is supposed to output "0" or "1" by exactly 50 to 50 chance. It generates a number of big arrays of 0s and 1s, each of which amounts to more than 4,000 of the numbers.
But because these arrays are physically generated in a fab, I assume it might develop some dependencies among numbers and some output could be predicted by more than 50% chance. For example, due to some variations in the process, "1" can be more likely to come than "0" after a sequence of "001100".
Then let's say I make a simple deep neural network which takes 7 inputs and gives 1 output. I simply slice my array by 8 numbers, 7 of which are given to the input and the last one is used as a label (the true answer). I train my simple DNN using all these sliced numbers and it will learn some sequences. Finally, I apply my NN to a test set, and if it predicts the next number with an accuracy of more than 50%, that proves my assumption, and if it doesn't that is also good for me because it says my circuits are good.
Would it work?