MIT: The ability for machines to reach the performance of the human brain in areas such as image interpretation or video may be able to yield a new kind of computer memory.
As reported in his publication MIT Technology Review, IBM researchers used the so-called phase-change memory για να δημιουργήσουν μια συσκευή η οποία επεξεργάζεται δεδομένα με τρόπο ο οποίος παραπέμπει στη λειτουργία ενός βιολογικού εγκεφάλου. Μέσω της χρήσης ενός πρωτότυπου phase-change memory chip, οι ερευνητές καθόρισαν ρύθμισαν το σύστημα έτσι ώστε να λειτουργήσει ως ένα δίκτυο 913 νευρώνων με 165.000 συνδέσεις, ή συνάψεις, μεταξύ αυτών.
The power of these synapses changes as the chip processes incoming data, changing how virtual neurons affect each other. Taking advantage of this feature, scientists have made the system capable of recognizing handwritten numbers.
The phase-change memory expected to enter the market within the next few years. It can record information at high speed and "pack" it into a much higher density than today's types of memory. A chip of such memory consists of a network of "cells" that can take two states to represent a digital bit informations- a 1 or a 0. In IBM's experimental system, each synapse is represented by a pair of cells working together.
"Brain" type computers are an item investigations by computer scientists for a long time. Such designs are radically different from today's chips, promising to make computers more efficient at tasks currently considered difficult by conventional systems—such as experiential learning or video understanding.
Earlier in the year, IBM announced the most advanced chip of its kind so far, created through techniques and components used to build smartphone processors. The new system is not as powerful, however, as Jeff Beer, an IBM researcher, pointed out that it is important that phase-change memory is used to create 165.000 synapses. According to him, this type of memory is considered to be suitable for "neuromorphic" type systems because it stores data at very high density - it is also easier to reprogram it. In practice, this facilitates the construction of a system that is "capable of" learning, by properly adjusting its behavior while receiving new data.
Previous attempts in this field were of limited scale, with 100 synapses or fewer. The new system, developed in collaboration with researchers at the Pohan Science and Technology University in Korea, is 1.000 of a larger size.
The paper was presented in December at the International Electron Devices Meeting in San Francisco.
Source: naftemporiki.gr
