Natural learning of neural networks by reconfiguration
The communicational and computational demands of neural networks are hard to satisfy in a digital technology. Temporal computing solves this problem by iteration, but leaves a slow network. Spatial computing was no option until the coming of modern FPGA devices. The letter shows how a small feed-forward neural module can be configured on the limited logic blocks between RAM and multiplier macro’s.