Abstract: Training a neural network according to a training algorithm, which may iteratively perform the following. Scaled connection weight values are called from a memory. Such values span an initial range within or compatible with the limited range of values allowed by hardware. Based on the values called, effective connection weight values are learned. The values learned span an effective range that differs from the initial range. As learning proceeds, the scaled connection weight values are updated by scaling the values learned, so as for the updated values to span a final range that is within the limited range. The training algorithm instructs to store the updated, scaled values on the memory, in view of a next iterative step.

Abstract: Training a neural network according to a training algorithm, which may iteratively perform the following. Scaled connection weight values are called from a memory. Such values span an initial range within or compatible with the limited range of values allowed by hardware. Based on the values called, effective connection weight values are learned. The values learned span an effective range that differs from the initial range. As learning proceeds, the scaled connection weight values are updated by scaling the values learned, so as for the updated values to span a final range that is within the limited range. The training algorithm instructs to store the updated, scaled values on the memory, in view of a next iterative step.