Computer chip mimics human brain with light beams for neurons

first_img Computer chip mimics human brain, with light beams for neurons sakkmesterke/iStockphoto Light rays can perform computations by interacting with each other. Sign up for our daily newsletter Get more great content like this delivered right to you! Country By Matthew HutsonJun. 20, 2017 , 4:00 PM Now, researchers at the Massachusetts Institute of Technology (MIT) in Cambridge have managed to condense much of that equipment to a microchip just a few millimeters across.The new chip is made of silicon, and it simulates a network of 16 neurons in four “layers” of four. Data enters the chip in the form of a laser beam split into four smaller beams. The brightness of each entering beam signifies a different number, or piece of information, and the brightness of each exiting beam represents a new number, the “solution” after the information has been processed. In between, the paths of light cross and interact in ways that can amplify or weaken their individual intensities, the same way ocean waves can add or subtract from each other when they cross. These crossings simulate the way a signal from one neuron to another in the brain can be intensified or dampened based on the strength of the connection. The beams also pass through simulated neurons that further adjust their intensities.Optical computation is efficient because once light rays are generated, they travel and interact on their own. You can guide them—without energy—using regular glass lenses, whereas transistors require electricity to operate.The researchers then tested their optical neural network on a real-world task: recognizing vowel sounds. When trained on recordings of 90 people making four vowel sounds, old-school computers performed the task with relative ease: A computer simulating a 16-neuron network was right 92% of the time. When the scientists tested the same data set on the new network, they came surprisingly close, with a success rate of 77% (but of course faster and more efficiently), they report this month in Nature Photonics. The researchers say they can improve performance with future adjustments.“Part of why this is new and exciting is that it uses silicon photonics, which is this new platform for doing optics on a chip,” says Alex Tait, an electrical engineer at Princeton University who was not involved in the work. “Because it uses silicon, it’s potentially low cost. They’re able to use existing foundries to scale up.” Tait and colleagues have also developed a partially optical neural net on a chip, which they plan to publish soon in Scientific Reports.Once the system includes more neurons and the kinks are worked out, it could supply data centers, autonomous cars, and national security services with neural nets that are orders of magnitude faster than existing designs, while using orders of magnitude less power, according to the study’s two primary authors, Yichen Shen, a physicist, and Nicholas Harris, an electrical engineer, both at MIT. The two are starting a company and hope to have a product ready in 2 years.center_img Country * Afghanistan Aland Islands Albania Algeria Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia, Plurinational State of Bonaire, Sint Eustatius and Saba Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Congo, the Democratic Republic of the Cook Islands Costa Rica Cote d’Ivoire Croatia Cuba Curaçao Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands (Malvinas) Faroe Islands Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Gambia Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and McDonald Islands Holy See (Vatican City State) Honduras Hungary Iceland India Indonesia Iran, Islamic Republic of Iraq Ireland Isle of Man Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Korea, Democratic People’s Republic of Korea, Republic of Kuwait Kyrgyzstan Lao People’s Democratic Republic Latvia Lebanon Lesotho Liberia Libyan Arab Jamahiriya Liechtenstein Lithuania Luxembourg Macao Macedonia, the former Yugoslav Republic of Madagascar Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mayotte Mexico Moldova, Republic of Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island Norway Oman Pakistan Palestine Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Qatar Reunion Romania Russian Federation Rwanda Saint Barthélemy Saint Helena, Ascension and Tristan da Cunha Saint Kitts and Nevis Saint Lucia Saint Martin (French part) Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Sint Maarten (Dutch part) Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Islands South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syrian Arab Republic Taiwan Tajikistan Tanzania, United Republic of Thailand Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, Bolivarian Republic of Vietnam Virgin Islands, British Wallis and Futuna Western Sahara Yemen Zambia Zimbabwe Artificial neural networks, computer algorithms that take inspiration from the human brain, have demonstrated fancy feats such as detecting lies, recognizing faces, and predicting heart attacks. But most computers can’t run them efficiently. Now, a team of engineers has designed a computer chip that uses beams of light to mimic neurons. Such “optical neural networks” could make any application of so-called deep learning—from virtual assistants to language translators—many times faster and more efficient.“It works brilliantly,” says Daniel Brunner, a physicist at the FEMTO-ST Institute in Besançon, France, who was not involved in the work. “But I think the really interesting things are yet to come.”Most computers work by using a series of transistors, gates that allow electricity to pass or not pass. But decades ago, physicists realized that light might make certain processes more efficient—for example, building neural networks. That’s because light waves can travel and interact in parallel, allowing them to perform lots of functions simultaneously. Scientists have used optical equipment to build simple neural nets, but these setups required tabletops full of sensitive mirrors and lenses. For years, photonic processing was dismissed as impractical. Click to view the privacy policy. Required fields are indicated by an asterisk (*) Emaillast_img

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