Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

We put a neural network inside the camera pixel. Introducing RETINA: a vision sensor that runs AI where the light lands

Synapse Semiconductor

1,204 subscribers

208,399 views • 3 months ago •via X (Twitter)

Arts Science & Technology

Anya Rossi• Live Now

Private livecam show

35 Comments

Sanjeev Chauhan3 months ago

We've designed some neat camera pixels that can run a CNN layer (matrix-matrix multiplication) using trapped charges in the pixel itself. These act as an image filter, letting only the important information in.

Cole3 months ago

I’m interested in building mini robots the size of baseballs to perform basic physical assistance tasks (camera / rear visibility, projection, and general fun at low cost). Lmk if yall wanna talk

Aidan Tiruvan3 months ago

better vision sensors def needed for the future of robotics. congrats!

Nolan Connolly3 months ago

Love the Steve Jobs iPhone reference

3DTOPO3 months ago

Uhhh Retina is a registered trademark by Apple

Roger Douglas Ware3 months ago

Imagine ai as a nural linguistic communication standard for pre cognitive lacing. Conciousness on a theta gamma band ai on an alpha/ beta channel. I'm putting ai into the stream of consciousness. Unless someone comes back in time to say stop it. Gpl-2 my work, my heart is rich. Money is discretionary. Play well with others.

Edward3 months ago

congrats!

devesh3 months ago

Holy banger 🔥

William Chen3 months ago

Life changing.

Daniel Keller3 months ago

Diffractive optical neural net?

Jeremy3 months ago

Can u put a chip in my eyes

Raul Izahi Lopez 🇺🇸3 months ago

Ready to make tens of millions of them?

Niki Kotecha3 months ago

love this guys!!!

steve3 months ago

Woa now

Kartikey Pandey3 months ago

@b1shtream Nice

Con3 months ago

Hate to say it but, I’ll believe it when I see it!!

Sarah Zhao3 months ago

LAUNCH DAY

Victor3 months ago

Let’s go 🚀

Dhruv Doshi3 months ago

Would love to learn more about this

Bruno Lima3 months ago

Let’s go!!

Ricky Chan3 months ago

Synapse, nice name.

Aubteen Pour-Biazar3 months ago

👏👏

Alex Pospekhov3 months ago

Insanely sick !!!!!!!!!!!!!!!!! Woah!!!!!

use.fo - AI Voice Typing3 months ago

The key is not just running AI on the sensor, but turning those raw signals into structured, usable language in real-time.

Kinjal Goswami3 months ago

Edge AI keeps getting crazier.

Adrian Thönig3 months ago

😂😂😂😂😂😂😂 bro that's exactly the steve jobs marketing pattern from the first iphone release.

LoBa._3 months ago

Coolest thing I’ve seen today

Priyav K Kaneria3 months ago

waow

Rey Neill3 months ago

Sorry?

Shubham3 months ago

When would be the best time to place an order? I'm looking forward to get one for a project I'm working on.

James Sackl3 months ago

Cool!

Tanay Lohia3 months ago

That be music is too distracting

homuraakemifan3 months ago

Stacking a camera sensor on top of an AI chip? Isn't that what China already does? Is it really that big of a deal that America is knocking it off? I guess the AI ballistic missile targeting system is very strong 🤔

Vihaan Shringi3 months ago

Cool

NovaBishop3 months ago

😳

Related Videos

A peek inside the mind of AI through neural network visualization.

A peek inside the mind of AI through neural network visualization.

Massimo

60,161 views • 9 months ago

Best visual I’ve seen of the inside of a Neural network, a CNN to be precise.

Best visual I’ve seen of the inside of a Neural network, a CNN to be precise.

Linus ✦ Ekenstam

174,554 views • 1 year ago

Introducing the next Raspberry Pi. An Ai model that runs on a credit card.

Introducing the next Raspberry Pi. An Ai model that runs on a credit card.

Founders Inc

157,395 views • 2 years ago

A Neural Network Can Grow New Neurons Where It Is Confused? In 1994, Bernd Fritzke published A Growing Neural Gas Network Learns Topologies. He introduced a network that starts small, follows incoming data, and inserts new neurons where its error is highest. In the animation, the fog is the drifting data. The glowing nodes are neurons. The fibers are learned connections. The network grows into a living skeleton of the manifold.

A Neural Network Can Grow New Neurons Where It Is Confused? In 1994, Bernd Fritzke published A Growing Neural Gas Network Learns Topologies. He introduced a network that starts small, follows incoming data, and inserts new neurons where its error is highest. In the animation, the fog is the drifting data. The glowing nodes are neurons. The fibers are learned connections. The network grows into a living skeleton of the manifold.

Mathelirium

38,431 views • 6 months ago

How far can a very simple eye go in solving vision tasks? Like a 1-pixel camera? Humans have one of the greatest eyes in nature, while many animals have significantly simpler eyes and visual systems yet show complex perceptual behavior. In an interesting project, we find that many computer vision tasks can be solved without a typical camera and with such simple 1-pixel sensors (photoreceptors). We also find that proper design (e.g., where to place the photoreceptors strategically) makes a big difference, so we developed a computational design method to find them. 🌐 👁️[Solving Vision Tasks with Simple Photoreceptors Instead of Cameras] 🧵1/n

How far can a very simple eye go in solving vision tasks? Like a 1-pixel camera? Humans have one of the greatest eyes in nature, while many animals have significantly simpler eyes and visual systems yet show complex perceptual behavior. In an interesting project, we find that many computer vision tasks can be solved without a typical camera and with such simple 1-pixel sensors (photoreceptors). We also find that proper design (e.g., where to place the photoreceptors strategically) makes a big difference, so we developed a computational design method to find them. 🌐 👁️[Solving Vision Tasks with Simple Photoreceptors Instead of Cameras] 🧵1/n

Amir Zamir

75,927 views • 2 years ago

OAK 4 CS now supports PTP synchronization. Sync multi-camera exposures over standard Ethernet. No FSYNC wiring, no daisy chains, no trigger cables across long distances. Just a shared clock distributed over the network. For multi-camera perception, distributed AI vision, and sensor fusion at scale.

OAK 4 CS now supports PTP synchronization. Sync multi-camera exposures over standard Ethernet. No FSYNC wiring, no daisy chains, no trigger cables across long distances. Just a shared clock distributed over the network. For multi-camera perception, distributed AI vision, and sensor fusion at scale.

Luxonis | Robotic Vision

32,787 views • 5 months ago

Introducing Pixel - a library for building GUIs in the terminal - 120 FPS pixel-perfect graphics - Embeddable inside existing TUIs - Built in chromium webview component

Introducing Pixel - a library for building GUIs in the terminal - 120 FPS pixel-perfect graphics - Embeddable inside existing TUIs - Built in chromium webview component

Rob Pruzan

115,631 views • 24 days ago

A single exchange––one that unifies liquidity, connects every network, and powers applications everywhere. One that can infinitely scale horizontally & vertically. With MetaDEX03 we finally have the OS to make that vision a reality: Introducing Aero. Coming soon to Ethereum.

A single exchange––one that unifies liquidity, connects every network, and powers applications everywhere. One that can infinitely scale horizontally & vertically. With MetaDEX03 we finally have the OS to make that vision a reality: Introducing Aero. Coming soon to Ethereum.

Dromos

418,487 views • 1 year ago

AI FOOTBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. You upload a regular match video. No sensors, no GPS trackers, just camera footage. The neural network finds every player, referee, and ball on its own. Every frame, in real time. KMeans clustering breaks down jersey colors pixel by pixel. The system splits players into teams automatically. Without a single manual hint. Optical Flow tracks camera movement. Separates it from player movement. Perspective Transformation converts pixels into real meters. Speed of every player. Distance covered. Ball possession percentage. All calculated automatically. Four hours of tutorial from zero to a working system. The model is trained on real Bundesliga matches. Runs on a regular GPU. Python code - take it and run. Sports analytics is no longer behind closed doors. AI leveled the playing field.

AI FOOTBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. You upload a regular match video. No sensors, no GPS trackers, just camera footage. The neural network finds every player, referee, and ball on its own. Every frame, in real time. KMeans clustering breaks down jersey colors pixel by pixel. The system splits players into teams automatically. Without a single manual hint. Optical Flow tracks camera movement. Separates it from player movement. Perspective Transformation converts pixels into real meters. Speed of every player. Distance covered. Ball possession percentage. All calculated automatically. Four hours of tutorial from zero to a working system. The model is trained on real Bundesliga matches. Runs on a regular GPU. Python code - take it and run. Sports analytics is no longer behind closed doors. AI leveled the playing field.

zostaff

1,078,520 views • 7 months ago

Today, we are releasing a research preview of Rho-1, our 19B omni model that understands and generates text, images, video and robot actions, all inside a single neural network.

Today, we are releasing a research preview of Rho-1, our 19B omni model that understands and generates text, images, video and robot actions, all inside a single neural network.

Reka

162,012 views • 1 month ago

Introducing Manako, the fastest way to turn any camera into an vision ai agent. Go on Join our waitlist.

Introducing Manako, the fastest way to turn any camera into an vision ai agent. Go on Join our waitlist.

Manako

27,970 views • 4 months ago

Introducing the new Pixel 10a. It has everything you need — like a completely flat, durable design, more battery life, advanced AI tools and a best-in-class camera system* — for just $499. *Among smartphones under $500 (MSRP) in the U.S. Based on third-party evaluation of Pixel 10a.

Introducing the new Pixel 10a. It has everything you need — like a completely flat, durable design, more battery life, advanced AI tools and a best-in-class camera system* — for just $499. *Among smartphones under $500 (MSRP) in the U.S. Based on third-party evaluation of Pixel 10a.

Google

85,535 views • 8 months ago

Today, Red Hat completed the acquisition of Red Hat AI (formerly Neural Magic), a pioneer in software and algorithms that accelerate #GenAI inference workloads. Read how we are accelerating our vision for #AI’s future:

Today, Red Hat completed the acquisition of Red Hat AI (formerly Neural Magic), a pioneer in software and algorithms that accelerate #GenAI inference workloads. Read how we are accelerating our vision for #AI’s future:

Red Hat

29,631 views • 1 year ago

Introducing Neural Tensor Dynamics (NTD): $NTD integrates a unique staking solution with high-yield $wTAO rewards, AI-driven DeFi tools, and validator services, all built atop the Bittensor network. Let’s explore these features in detail:

Introducing Neural Tensor Dynamics (NTD): $NTD integrates a unique staking solution with high-yield $wTAO rewards, AI-driven DeFi tools, and validator services, all built atop the Bittensor network. Let’s explore these features in detail:

Neural Tensor Dynamics

51,997 views • 2 years ago

Can a single neural network policy generalize over poses, objects, obstacles, backgrounds, scene arrangements, in-hand objects, and start/goal states? Introducing Neural MP: A generalist policy for solving motion planning tasks in the real world 🤖 1/N

Can a single neural network policy generalize over poses, objects, obstacles, backgrounds, scene arrangements, in-hand objects, and start/goal states? Introducing Neural MP: A generalist policy for solving motion planning tasks in the real world 🤖 1/N

Murtaza Dalal

114,942 views • 2 years ago

Implemented a visualization for the neural network today. This is a neural network being trained to learn the snake game in a terminal. It's built in Rust using Ratatui More input details in thread

Implemented a visualization for the neural network today. This is a neural network being trained to learn the snake game in a terminal. It's built in Rust using Ratatui More input details in thread

Bones

165,760 views • 2 years ago