Abstract: Logic may reduce the size of runtime memory for deep neural network inference computations. Logic may determine, for two or more stages of a neural network, a count of shared block allocations, or shared memory block allocations, that concurrently exist during execution of the two or more stages. Logic may compare counts of the shared block allocations to determine a maximum count of the counts. Logic may reduce inference computation time for deep neural network inference computations. Logic may determine a size for each of the shared block allocations of the count of shared memory block allocations, to accommodate data to store in a shared memory during execution of the two or more stages of the cascaded neural network. Logic may determine a batch size per stage of the two or more stages of a cascaded neural network based on a lack interdependencies between input data.

Abstract: Logic may reduce the size of runtime memory for deep neural network inference computations. Logic may determine, for two or more stages of a neural network, a count of shared block allocations, or shared memory block allocations, that concurrently exist during execution of the two or more stages. Logic may compare counts of the shared block allocations to determine a maximum count of the counts. Logic may reduce inference computation time for deep neural network inference computations. Logic may determine a size for each of the shared block allocations of the count of shared memory block allocations, to accommodate data to store in a shared memory during execution of the two or more stages of the cascaded neural network. Logic may determine a batch size per stage of the two or more stages of a cascaded neural network based on a lack interdependencies between input data.