Researchers at the National Institute of Standards and Technology (NIST) have made a silicon chip that distributes optical signals precisely across a miniature brain-like grid, showcasing a potential new design for neural networks.
The human brain has billions of neurons (nerve cells), each with thousands of connections to other neurons. Many computing research projects aim to emulate the brain by creating circuits of artificial neural networks. But conventional electronics, including the electrical wiring of semiconductor circuits, often impedes the extremely complex routing required for useful neural networks.
The NIST team proposes to use light instead of electricity as a signaling medium. Neural networks already have demonstrated remarkable power in solving complex problems, including rapid pattern recognition and data analysis. The use of light would eliminate interference due to electrical charge, and the signals would travel faster and farther.
“Light’s advantages could improve the performance of neural nets for scientific data analysis such as searches for Earth-like planets and quantum information science, and accelerate the development of highly intuitive control systems for autonomous vehicles,” NIST physicist Jeff Chiles said.
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 recognize certain patterns of stimuli. A neural or neuromorphic computer would consist of a large, complex system of neural networks.
Described in a new paper, the NIST chip overcomes a major challenge to the use of light signals by vertically stacking two layers of photonic waveguides—structures 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.
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 fiber. 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. The authors 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, researchers 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 analyzed at the same time with high precision. The output was highly uniform, with low error rates, confirming precise power distribution.
“We’ve really done two things here,” Chiles said. “We’ve begun to use the third dimension to enable more optical connectivity, and we’ve developed a new measurement technique to rapidly characterize many devices in a photonic system. Both advances are crucial as we begin to scale up to massive optoelectronic neural systems.”
Learn more: NIST Chip Lights Up Optical Neural Network Demo
The Latest on: Optical neural network
via Google News
The Latest on: Optical neural network
- Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Predictionon November 14, 2019 at 2:58 am
The two methods which are not significant better than ZeroR are the two-stream neural networks, which combined the two optical flow representations in a custom network. We hypothesize that this is ...
- NTT Research to Work with Caltech, Cornell, Michigan, MIT, NASA, Stanford, Swinburne, and 1QBiton November 13, 2019 at 3:17 pm
“Having launched only four months ago, we are excited to have reached agreements with eight of the world’s top research organizations with interests and expertise in the three fields crucial to our ...
- NTT taps NASA to take on Google and IBM in quantum computingon November 13, 2019 at 1:11 pm
Gate- and annealing-based quantum computers can only operate in ultralow temperatures, but NTT's optical network-based system allows a machine to work at room temperature. However, some researchers ...
- Police to use AI recognition drones to help find the missingon November 4, 2019 at 5:13 am
It uses advanced cameras and neural computer networks to spot someone it is looking for - from ... "There's a very highly-powered optical camera which can allow us to see things quite clearly from a ...
- Brit police launch spy drone ‘flying squad’ to find missing peopleon November 4, 2019 at 4:39 am
The remotely-piloted aircraft system (RPAS) is said to use advanced cameras and neural computer networks. These features allow the drones to spot people ... "There's a very highly-powered optical ...
- Wanna dance like a pro? A neural network can help youon October 19, 2019 at 11:19 pm
Last but not least, we will introduce temporal influence from adjacent frames and optical flows of frames which are crucial for the temporal smoothness of the generated videos. How to do the ...
- Novel nanoprobes show promise for optical monitoring of neural activityon October 18, 2019 at 12:23 pm
Researchers at UC Santa Cruz have developed ultrasensitive nanoscale optical probes to monitor the bioelectric activity of neurons and other excitable cells. This novel readout technology could enable ...
- All-Optical Neural Network Uses Parallel Computation to Speed Problem-Solvingon August 30, 2019 at 9:07 am
In a step toward making the use of large-scale optical neural networks practical, researchers at The Hong Kong University of Science and Technology have demonstrated a multilayer all-optical ...
- All-optical neural network for deep learningon August 29, 2019 at 7:30 am
In a key step toward making large-scale optical neural networks practical, researchers have demonstrated a first-of-its-kind multilayer all-optical artificial neural network. Researchers detail their ...
via Bing News