Stochastic computing with generated deterministic sequences

A computer program product, the computer program product comprising one or more computer readable storage media and collectively stored program instructions on the one or more computer readable storage media, the stored program instructions which, when executed, cause one or more computer systems to:

• receive a trained neural network having a set of defined node weights;
• determine a set of network activations according to network input data;
• for each activation x of the set of network activations and each node weight w of the set of defined node weights:
• generate an activation sequence X=(Xnx, Xnx−1, . . . , X2, X1, X0) which is a unary representation of x/nx, a first weight sequence WL=(W, W, . . . , W) by repeating W for 1w/nw times, and a second weight sequence WR=(V, V, . . . , V) by repeating V for nx/nw times, where the sequence W=(Wnw, Wnw−1, . . . , W2, W1, W0) is a unary representation of w/nw, the sequence V=(W0, W1, W2, . . . , Wnw−1, Wnw) is the reverse of W, and nx is a multiple of nw so that WL and WR are of length nx;
• and
• compute MS(X, WL)+MS(X, WR) as an approximation for 2(x/nx)(w/nw),
• wherein MS(.,.) denotes stochastic multiplication;
• and
• provide a neural network output based on, at least in part, a set of computed approximations for 2(x/nx)(w/nw) values.

A computer system for providing neural network inference outputs, the computer system comprising:

• one or more computer processors;
• one or more computer readable storage devices;
• and
• stored program instructions on the one or more computer readable storage devices for execution by the one or more computer processors, the stored program instructions which, when executed, cause the one or more computer processors to:
• receive a trained neural network having a set of defined node weights;
• determine a set of network activations according to network input data;
• for each activation x of the set of network activations and each node weight w of the set of defined node weights:
• generate an activation sequence X=(xnx) Xnx−1, . . . , ×2, x1, X0) which is a unary representation of x/nx, a first weight sequence WL=(W, W, . . . , W) by repeating W for 11w/nw times, and a second weight sequence WR=(V, V, . . . , V) by repeating V for nx/nw times, where the sequence W=(Wnw, Wnw−1, . . . , W2, W1, W0) is a unary representation of w/nw, the sequence V=(w0, W1, W2, . . . , Wnw−1, Wnw) is the reverse of W, and nx is a multiple of nw so that WL and WR are of length nx;
• and
• compute MS(X, WL)+MS(X, WR) as an approximation for 2(x/nx)(w/nw),
• wherein MS(.,.) denotes stochastic multiplication;
• and
• provide a neural network output based on, at least in part, a set of computed approximations for 2(x/nx)(w/nw) values.