  Neuromorphic engineering has led to the necessary process of rethinking of
how we process and integrate information, analyze data, and use the resulting
insights to improve computation and avoid the current high power and latency of
Artificial Intelligence (AI) hardware. Current neuromorphic processors are,
however, limited by digital technologies, which cannot reproduce the abilities
of biological neural computation in terms of power, latency and area cost. In
this paper, we show that the combined use of the dynamic properties of
memristors to implement a model of synaptic integration and the determination
of the correct level of abstraction of biological neural networks has the
potential to open a new range of capabilities for neuromorphic processors. We
test this approach using a novel three-terminal LixWO3 electrochemical
memristor, by deriving its conductance model and using it to emulate synaptic
temporal kernel computation in the context of a pattern recognition task. We
show that these devices allow for robust results with no loss in precision
while opening the path for an energy efficient approach to build novel
bio-inspired processing units in silicon.

Emerging Technologies