This optical brain like computer chip analyses almost 2 billion images per second
In the future machine vision will capture even more videos and images than it does today, and synthetic content will create even more, and we need computer chips that can process this insane volume of graphical data.
Key takeaways
- In tests, the team made a chip measuring 9.3 mm2 (0.01 in2) and put it to work categorizing a series of handwritten characters that resembled letters.
- After being trained on relevant data sets, the chip was able to classify the images with 93.8 percent accuracy for sets containing two types of characters, and 89.8 percent accuracy for four types.
- Most impressively, the chip was able to categorize each character within 0.57 nanoseconds, which would allow it to process 1.75 billion images per second.
Cite or link to this article
Griffin, M. (2022) 'This optical brain like computer chip analyses almost 2 billion images per second', 311 Institute, 14 July. Available at: https://www.311institute.com/this-optical-brain-like-computer-chip-analyses-almost-2-billion-images-per-second/ (Accessed: 1 October 2026).
How many images can your brain – the brain in your head that many say is “the most complex thing in the universe” – process? A couple of hundred per second, a few thousand? Well now your puny mind has been bested good and proper after researchers at the University of Pennsylvania announced they’ve developed a powerful new optical chip for futuristic photonic computing systems that can process almost 2 billion images per second. The device is made up of a neural network that processes information as light without needing components that slow down traditional computer chips, like memory.
The basis of the new chip is a neural network, a system modelled on the way the brain processes information. These networks are made up of nodes that interconnect like neurons, and they even “learn” in a similar way to organic brains by being trained on sets of data, such as recognizing objects in images or words in speech. Over time, they become much better at these tasks.
But rather than electrical signals, the new chip processes information in the form of light. It uses optical wires as its neurons, stacked in multiple layers that each specialize in a particular type of classification.
In tests, the team made a chip measuring 9.3 mm2 (0.01 in2) and put it to work categorizing a series of handwritten characters that resembled letters. After being trained on relevant data sets, the chip was able to classify the images with 93.8 percent accuracy for sets containing two types of characters, and 89.8 percent accuracy for four types.
Most impressively, the chip was able to categorize each character within 0.57 nanoseconds, which would allow it to process 1.75 billion images per second. The team says that this speed comes from the chip’s ability to process information as light, which gives it several advantages over existing computer chips.
“Our chip processes information through what we call ‘computation-by-propagation,’ meaning that unlike clock-based systems, computations occur as light propagates through the chip,” said Firooz Aflatouni, lead author of the study.
“We are also skipping the step of converting optical signals to electrical signals because our chip can read and process optical signals directly, and both of these changes make our chip a significantly faster technology.”
Another advantage is that the information being processed doesn’t need to be stored, so it also saves time by not having to send data to memory, and space in not needing a component for memory at all. The team also says that not storing the data is also more secure, since it prevents any possible leaks.
The next steps for the team are to begin scaling up the chip, and adapting the technology to process other types of data.
“What’s really interesting about this technology is that it can do so much more than classify images,” said Aflatouni. “We already know how to convert many data types into the electrical domain – images, audio, speech, and many other data types. Now, we can convert different data types into the optical domain and have them processed almost instantaneously using this technology.”
All of which means that soon, very soon, the most complex thing in the universe might be a bunch of silicon wafers and an artificial intelligence …
The research was published in the journal Nature.
Source: University of Pennsylvania
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Why does this matter?
In the future machine vision will capture even more videos and images than it does today, and synthetic content will create even more, and we need computer chips that can process this insane volume of graphical data.

About the author
Matthew Griffin Founder, 311 Institute
Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath."
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Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath." 15-time best selling author of the "Codex of the Future" series, Matthew is the Founder and Futurist in Chief of the 311 Institute, a global Futures and Deep Futures advisory firm working with royal households, world leaders, G7, G20, and G77 governments, NGOs, and multi-national mid and mega cap firms to help them explore, shape, and lead the next 50 years of business and society.
An award-winning YouTube creator with over a million followers, with an unrivalled global reach and impact, Matthew is a highly sought-after international keynote speaker, lecturer, and mentor who collaborates with global leaders through the United Nations Alliance of Civilizations (UNAOC) and United Nations General Assembly (UNGA) to shape pivotal initiatives such as the UN’s AI for Humanity program, the United Nations Conference of the Parties (UN COP), and the World Economic Forum in Davos.
As the former Global Head of Cloud, National Security, and Enterprise Sales for companies including Atos, Dell-EMC, and IBM, Matthew has a proven track record of building multi-billion dollar business units and turning failing divisions into market leaders. His ability to identify, analyse, and communicate the implications of hundreds of emerging technologies and trends is unparalleled, and his insights are trusted by many of the world’s most respected organisations, including ABB, Accenture, Adidas, AON, ARM, BCG, Centrica, Citi, Coca-Cola, Dentons, Deloitte, Dow Jones, EY, Google, KPMG, Lego, Legal & General, LinkedIn, Microsoft, PepsiCo, Qualcomm, RWE, Samsung, Siemens AG and Siemens Energy, T-Mobile, UBS, VISA, Walmart, Workday, Worldpay and many others.
Regularly featured in the global media including the AP, BBC, Bloomberg, CNBC, Discovery, Forbes, Khaleej Times, Telegraph, TIME, ViacomCBS, WIRED, and the WSJ, Matthews mission is to help organisations create a fair and sustainable future whose benefits are shared by everyone irrespective of their ability, background, or circumstances.
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Sources and further reading
- S41586 022 04714 0 nature.com
- University of Pennsylvania blog.seas.upenn.edu
Source: first published by the 311 Institute on 14 July 2022. Cite as: Griffin, M. (2022). This optical brain like computer chip analyses almost 2 billion images per second. 311 Institute. https://www.311institute.com/this-optical-brain-like-computer-chip-analyses-almost-2-billion-images-per-second/
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