Leti, a technology research institute of CEA Tech, has fabricated a fully integrated bio-inspired neural network, combining resistive-RAM-based synapses and analog spiking neurons. Resistive-RAM (RRAM) is a type of non-volatile random-access computer memory that works by changing the resistance across a dielectric solid-state material.
Spiking neural networks are composed of bio-inspired neurons. They communicate by emitting spikes, or discrete events that take place at a point in time, rather than continuous values. These networks promise to further reduce required computational power because they use less complex computing operations, e.g. additions instead of multiplications. They also inherently exploit sparsity of input events, since they are intrinsically event-based.
The research work presented at IEDM 2019 measured a 5x reduction in energy use compared to an equivalent chip using formal coding. The neural network implementation is made such that synapses are placed close to neurons, which enables direct synaptic current integration.
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