Linear transforms — like a Fourier transform — are a key math tool in engineering and science. A team from UCLA recently published a paper describing how they used deep learning techniques to design an all-optical solution for arbitrary linear transforms. The technique doesn’t use any conventional processing elements and, instead, relies on diffractive surfaces. They also describe a “data free” design approach that does not rely on deep learning.
There is obvious appeal to using light to compute transforms. The computation occurs at the speed of light and in a highly parallel fashion. The final system will have multiple diffractive surfaces to compute the final result.
Continue reading “Fourier Transforms (and More) Using Light”
We’ve all come to terms with a neural network doing jobs such as handwriting recognition. The basics have been in place for years and the recent increase in computing power and parallel processing has made it a very practical technology. However, at the core level it is still a digital computer moving bits around just like any other program. That isn’t the case with a new neural network fielded by researchers from the University of Wisconsin, MIT, and Columbia. This panel of special glass requires no electrical power, and is able to recognize gray-scale handwritten numbers.
Continue reading “Neural Network In Glass Requires No Power, Recognizes Numbers”
Neural networks ought to be very appealing to hackers. You can easily implement them in hardware or software and relatively simple networks can perform powerful functions. As the jobs we ask of neural networks get more complex, the networks require more artificial neurons. That’s why researchers are pursuing dense integrated neuron chips that could do for neural networks what integrated circuits did for conventional computers.
Researchers at Princeton have announced the first photonic neural network. We recently talked about how artificial neurons work in conventional hardware and software. The artificial neurons look for inputs to reach a threshold which causes them to “fire” and trigger inputs to other neurons.
To map this function to an optical device, the researchers created tiny circular waveguides in a silicon substrate. Light circulates in the waveguide and, when released, modulates the output of a laser. Each waveguide works with a specific wavelength of light. This allows multiple “inputs” (in the form of different wavelengths) to sum together to modulate the laser.
The team used a 49-node network to model a differential equation. The photonic system was nearly 2,000 times faster than other techniques. You can read the actual paper online if you are interested in more details.
There’s been a lot of work done lately on both neural networks and optical computing. Perhaps this fusion will advance both arts.
One challenge to building optical computing devices and some quantum computers is finding a source of single photons. There are a lot of different techniques, but many of them aren’t very practical, requiring lots of space and cryogenic cooling. Recently, researchers at the Hebrew University of Jerusalem developed a scalable photon source on a semiconductor die.
Using nanocrystals of semiconductor material, the new technique emits single photons, and in a predictable direction. The nanocrystals combine with circular nanoantennas made of metal and dielectric produced with conventional fabrication technology. The nanoantennas are concentric circles resembling a bullseye and is used to ensure that the photons travel the correct direction with little or no angular deviation.
A single IC could contain many photon sources and they operate at room temperature. We’ve talked about quantum tricks with photons before. Quantum mechanics is another popular topic.