Optical Computing and Photonics Processor

Optical or photonic computing uses photons produced by lasers or diodes for computation.

In late 2015, researchers at the Univeristy Of Colorado created a first full fledged light processor which will transmit data using light instead of electricity. It has 850 I/O elements that give it a bandwidth way more than electric chips, we’re talking 300Gbps per square millimeter or 10 to 50 times what you normally see, replacing the circuitry with optics.


Although having a size of 3mm x 2mm and just two cores, it has dramatic potential over our normal processor.


“It’s the first processor that can use light to communicate with the external world,” Vladimir Stojanović, the University of California professor who led the collaboration, said in a press release.


Photonic Logic:

Photonic logic is the use of photons (light) in logic gates (NOT, AND, OR, NAND, NOR, XOR, XNOR). Switching is obtained using nonlinear optical effects when two or more signals are combined. Resonators are especially useful in photonic logic, since they allow a build-up of energy from constructive interference, thus enhancing optical nonlinear effects.

It’s been said that we may see optical computing soon. It’s because light pulses are used to send data instead of voltage packets. Processor will change from binary to light pulses using lasers

So what about memory?

A holographic memory can store data in the form of a hologram within a crystal. A laser is split into a reference beam and a signal beam. Signal beam goes through the logic gate and receives information. The two beams then meet up again and interference pattern creates a hologram in the crystal.


Holographic Memory


Advantages of Optical Computing:

  • Small size
  • Increased speed
  • Low heating
  • Reconfigurable
  • Scalable for larger or small networks
  • More complex functions done faster
  • Applications for Artificial Intelligence
  • Less power consumption (500 microwatts per interconnect length vs. 10 mW for electrical)




Artificial Neural Networks ( ANN )

So what’s an Artificial Neural Network ?

Artificial Neural Networks ( ANNs ) are relatively crude electronic models based on the neural stucture of the brain. As the brain basically learns from experience, It is natural proof that some problems that are beyond the scope of current computers are indeed solvable by small energy efficient packages. This brain modeling also promises a less technical way to develop machine solutions. This new approach to computing also provides a more graceful degradation during system overload than its more traditional counterparts. These biological inspired methods of computing are thought to be next major advancement in computer industry.


Most neurons in the brain are connected to several thousand others.

So how does it work?

An artificial neural network is simulated with software. In other words, we use a digital computer to run a simulation of a bunch of heavily interconnected little mini-programs which stand in for the neurons of our simulated neural network. Data enters the ANN and has some operation performed on it by the first “neuron,” that operation being determined by how the neuron happens to be programmed to react to data with those specific attributes. It’s then passed on to the next neuron, which is chosen in a similar way, so that another operation can be chosen and performed. There are a finite number of “layers” of these computational neurons, and after moving through them all, an output is produced.


Natural Neuron


An Artificial Neuron


So where is it used?

  • Google uses an ANN to learn how to better target “watch next” suggestions after YouTube videos.
  • The scientists at the Large Hadron Collider turned to ANNs to sift the results of their collisions and pull the signature of just one particle out of the larger storm.
  • Shipping companies use them to minimize route lengths over a complex scattering of destinations.

Google’s new Smart Reply feature uses artificial neural networks to come up with appropriate responses to email messages.

“That first decision is made by an artificial neural network very much like the ones we use for spam classification and separating promotional emails from personal ones,” says Greg Corrado, senior research scientist on the Google Brain Team. “Our network has been trained to predict whether this is an email someone might write a brief reply to.” For the full article on Wired (http://www.wired.com/2016/03/google-inbox-auto-answers-emails/?mbid=social_gplus)

So concluding this blog we’ve put some light on ANNs and also given a brief information on it. If you like this blog and would like some more related topics do leave a comment!