Circuits of Artificial Neural Network to Analyse Data

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Computing research projects aim to imitate the brain by creating circuits of artificial neural networks to analyse data

Researchers at the National Institute of Standards and Technology (NIST) have made a silicon chip that distributes optical signals across a miniature brain-like grid, showcasing a potential new design for neural networks. They used light instead of electricity to eliminate interference due to electrical charge and the signals would travel faster and farther.

Recognise pattern of stimuli

A conventional computer processes information through algorithms or human-coded rules. By contrast, a neural network relies on a network of connections among processing elements or neurons which can be trained to recognise certain patterns of stimuli. A neural or neuromorphic computer would consist of a large, complex system of neural networks.

The NIST chip uses the light signals by vertically stacking two layers of photonic waveguides that confine light into narrow lines for routing optical signals, much as wires route electrical signals. This three-dimensional (3D) design enables complex routing schemes, which are necessary to mimic neural systems. Furthermore, this design can easily be extended to incorporate additional waveguiding layers when needed for more complex networks.

To generate signal routing

The stacked waveguides form a three-dimensional grid with 10 inputs or “upstream” neurons each connecting to 10 outputs or “downstream” neurons, for a total of 100 receivers. Fabricated on a silicon wafer, the waveguides are made of silicon nitride and are each 800 nanometers (nm) wide and 400 nm thick. Researchers created software to automatically generate signal routing, with adjustable levels of connectivity between the neurons.

Laser light was directed into the chip through an optical fibre. The goal was to route each input to every output group, following a selected distribution pattern for light intensity or power. Power levels represent the pattern and degree of connectivity in the circuit. They demonstrated two schemes for controlling output intensity: uniform (each output receives the same power) and a “bell curve” distribution (in which middle neurons receive the most power, while peripheral neurons receive less).

To evaluate the results, they made images of the output signals. All signals were focused through a microscope lens onto a semiconductor sensor and processed into image frames. This method allows many devices to be analysed at the same time with precision and the output was uniform, with low error rates and also confirmed power distribution.