Arxiv – Optoelectronic Reservoir Computing (39 pages) A new approach is much faster than prior attempts at this style of computing but it is not known if it will become faster than regular electronic computing techniques.
Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations
Technology Review on Reservoir Computing –
Here the reservoir consists of a reasonably large number of nodes that are connected together at random. Each node is some kind of non-linear feedback loop. The input, or inputs, are fed into random nodes in the reservoir and the output, or outputs, taken from other randomly chosen nodes. The system is then trained to produce the desired computation by weighting the outputs in a certain way. For example, the input might consist of a waves of certain shapes and the output would be an indication that specific shapes had been recognised.
It is similar to neural networks. However, the important difference is that the reservoir works essentially like a black box. Only the output signals are weighted during training, making this process much simpler than with a neural network, which are notoriously difficult to fine tune.
The remarkable speed and multiplexing capability of optics makes it very attractive for information processing. These features have enabled the telecommunications revolution of the past decades. However, so far they have not been exploited insomuch as computation is concerned. The reason is that optical nonlinearities are very difficult to harness: it remains challenging to just demonstrate optical logic gates, let alone compete with digital electronics. This suggests that a much more flexible approach is called for, which would exploit as much as possible the strenghts of optics without trying to mimic digital electronics. Reservoir computing, a recently introduced, bio-inspired approach to artificial intelligence, may provide such an opportunity
Their reservoir consists of 50 nodes, connected together at random, with the readout taken from one node.They trained this device to perform a number of tasks such as distinguishing between sine waves and square waves and even simple word recognition.
This task involves five female voices speaking the digits 1-9, which the computer has to recognise. Paquot and co’s device did this with an error rate of just 0.4 per cent. In other words, 2 errors in 500 recognised words.
That’s not bad but what’s really impressive is the speed at which it does this task. “Our experiment is the ﬁrst implementation of reservoir computing fast enough for real time information processing.
This version is almost 6 orders of magnitude faster than the earlier attempt and that a further speed increase of 2-3 orders of magnitude should be possible using various new, off-the-shelf optoelectronic components.
If you liked this article, please give it a quick review on ycombinator or StumbleUpon. Thanks
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.