Neuromorphic Computing From Materials to Systems Architecture

Once hundreds of neurons and tens of thousands of synapses have been demonstrated in a
novel system, it may be straightforward to scale these building blocks to the scale of
systems competitive with the largest CMOS implementations.
State-­‐‐of-­‐‐the-­‐‐art neural networks that support object and speech recognition can have tens
of millions of synapses and networks with thousands of inputs and thousands of outputs.
Simple street-­‐‐scene recognition needed for autonomous vehicles require hundreds of
thousands of synapses and tens of thousands of neurons. The largest networks that have
been published—using over a billion synapses and a million neurons—have been used for
face detection and object recognition in large video databases.
Figure 6. Block diagram of a hybrid neuromorphic processor for synapse materials testing. The idea is
that novel materials could be tested in a “harness” that uses existing CMOS implementations of learning and
soma. A framework such as this could be used to accelerate testing of materials at some modest scale.
Properties
Development of neuromorphic computers, materials and/or devices are needed that
exhibit some (or many) of the following properties:
1. Multistate behavior, in which a physical property may have different values for the
same control parameters, depending on past history.
2. Sensitivity to external stimuli such as current, voltage, light, H field, temperature or
pressure to provide desirable functionalities.
Neuromorphic Computing: From Materials to Systems Architecture
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