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I remember, it was a long time ago, the discussion on the advantage of going digital in sound coding (and then reproduction) and we actually ended up with digital winning hands down in all the music sector. Still a number of people resisted the idea claiming that analogue remained better for the continuous nuances it could provide, versus the chopping characteristics you get when converting a wave into a series of zeros and ones. The digital supporters said, with a certain reason, that if the chopping caused by digitalisation was below our human detection capability (you can’t tell the difference) well it was a moot discussion. Still the analogue folks maintained their position.
This came to my mind as I saw a paper presenting the results of researchers at MIT on creating an analogue chip to perform image recognition (specifically tested on handwriting recognition). Our brain is basically an analogue structure, although over and over we are trying to relate neurones and synapses to transistors and bits. The idea is that a synapse triggers a signal or it does not trigger it, a neurone fires or does not fire. These map nicely onto 0 and 1. The situation, however, is quite more fuzzy and this mapping into 0s and 1s is not perfect.
In our brain there is a computation based on the strength of impulses that does not match with the conversion into 0s and 1s. There is quite a bit in between.
We have seen a number of neuromorphic chips, like IBM SyNAPSE, that aims at mimicking the brain neuronal structure/operation and they perform quite well. We have seen progress in memristors, where memory and computation merges like it happens, in a way, in a neurone. All of these have led to better way of computation particularly in areas like artificial intelligence -deep learning- applied to problems like image recognition. But we are not there, yet.
In these neuromorphic chips the exchange among the various parts does not occur in terms of 0s and 1s, rather through gradient of current. The problem is the difficulty in controlling these various gradients. The MIT researchers have been able to create a chip made with silicon germanium that is able to control accurately the flow of current. Instead of using an amorphous material as synapse in the chip the researchers have designed an epitaxial random access memory creating a structure partly based on germanium and partly on silicon. These two materials create structures with slightly different dimensions and this difference is used to obtain a funnel through which ions (current) can flow in a very controllable way.
They have tested the chip in the handwriting recognition obtaining an accuracy of 95%, an amazingly good result.
Interestingly, this chip can make portable neural network devices possible, which means that in certain domains, like image recognition (a most important area for autonomous systems, like self driving cars), computations that today require a supercomputer would become possible in the palm of our hand.
