A Spiking Neuromorphic Architecture Using Gated-RRAM for Associative Memory

This work reports a spiking neuromorphic architecture for associative memory simulated in a SPICE environment using recently reported gated-RRAM (resistive random-access memory) devices as synapses alongside neurons based on complementary metal-oxide semiconductors (CMOSs). The network utilizes a Verilog A model to capture the behavior of the gated-RRAM devices within the architecture. The model uses parameters obtained from experimental gated-RRAM devices that were fabricated and tested in this work. Using these devices in tandem with CMOS neuron circuitry, our results indicate that the proposed architecture can learn an association in real time and retrieve the learned association when incomplete information is provided. These results show the promise for gated-RRAM devices for associative memory tasks within a spiking neuromorphic architecture framework.

Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO2 material, and we propose a methodology to map Hebbian coefficients to ONN coupling resistances, allowing a large-scale ONN design. We develop an analytical framework to map weight coefficients into coupling resistor values to analyze ONN architecture performance. We report on an ONN with 60 fully-connected oscillators that perform pattern recognition as a Hopfield Neural Network.

When buildings have “brains”

Neuromorphic architecture, buildings with “brains,” revisits Le Corbusier’s “a house is a machine for living in” in cybernetic terms. It relates to smart architecture and artificial intelligence and is informed by neuroethology. The interactive space Ada had sensors for vision, audition, and a sense of touch mediated by floor panels. Ada could use this information to interact with visitors. Ada had “emotions,” and this leads into analysis of how “emotion” in buildings and robots relates to the neuroscience of emotions. The chapter distinguishes the physical space of the building from the neural space that connects sensors and effectors to support homeostasis and social interaction between building and users. The example of a neuromorphic kitchen grounds discussion of how buildings may learn. Such buildings can be considered as inside-out robots or embrained bodies. Biophilia and the way humans flourish when they have a sense of community are exemplified in WOHA’s Kampung Admiralty project, which functions as a system of systems. After considering a building as a system of systems, one may assess which of those subsystems may be ripe for being cybernetically enabled.