AN ANALOG NEURONLESS RECONFIGURABLE FEED FORWARD NEURAL NETWORK
Mario Costa - Davide Palmisano - Eros Pasero
Today Feed Forward neural Networks (FFNs) use paradigms tied to
mathematical frame-works really hard to be transferred in a hardware
realisation. This fact makes analog neural integrated circuits heavy to
design. Here we propose an alternative formal and general model that
can use the native computational properties of the basic electronic
circuits without any previous assumption about their behavior.
A practical framework is described to train such analog FFNs off-chip.
This is especially useful whenever the weight storage elements cannot
be re-programmed on the fly at a high rate. Then we will present how
this formal model allowed us the development of innovative and non
conventional neural architectures we named "neuron-less" (N-LESS).
Keywords: analog neural networks, analog integrated circuits, analog memories, analog signal processing