This transcript explores the concept of optical computing as an alternative to traditional electrical computing, highlighting its potential for near-instantaneous, low-power processing. The discussion focuses on Lightmatter, a company that has developed a photonic chiplet called "Envise" designed for optical compute, leveraging an optical interposer technology. The speaker details the physical aspects of the Envise package and PCIe card, which integrates on-card lasers and photonic cores for matrix vector products. A key demonstration is presented: the Envise chip running an Atari game. It's clarified that the chip isn't running the game itself but acts as an AI player, processing video output via a convolutional neural network to determine game actions. While showcasing impressive early capabilities in neural network inference, the speaker acknowledges current limitations in scalability and the challenges of converting data between electrical and optical forms, positioning photonic computing as still in its nascent stages compared to other emerging paradigms like quantum computing.
Summarizing "Optical Compute: A Game Changer?"
0:00 Electrical vs. Optical Computing: The video begins by contrasting traditional electrical computing, which relies on transistors and switches, with the emerging field of optical computing, which uses light.
0:46 Advantages of Optical Computing: Optical computing offers the potential for near-instantaneous compute speeds, as light interacts at the speed of light, promising ultra-low latency and low power consumption, especially for operations like addition and multiplication.
1:14 History and Challenges: Optical compute research has existed for a long time, with many startups emerging and disappearing. Challenges include the need for larger structures compared to nanometer-scale transistors, difficulty integrating on-chip or off-chip lasers, and efficient electrical-to-optical data conversion.
1:29 Lightmatter and Envise: Lightmatter, originally an optical compute company that pivoted to networking, has continued developing optical compute, announcing their "Envise" optical chiplet about three years prior.
3:18 Envise Hardware Description: The Envise package resembles a standard BGA processor but integrates optical fibers for input and output. It's designed as a PCIe accelerator card with on-card lasers for light input and features four photonic cores with 128x128 systolic arrays for matrix vector products, targeting advanced neural network models.
4:21 The Atari Demo: A demo called "Atari" is presented, where the optical hardware plays an Atari game. Crucially, the Envise chip is not running the game itself, but acting as an AI player.
5:58 How the Atari Demo Works: The game's video output (running on a CPU) is fed into a neural network model on the Envise photonic processor. Envise then decides the next action (e.g., left, right, fire) and sends it back to the game, effectively pressing buttons on a virtual controller.
7:02 Neural Network Model Details: The system uses a basic convolutional neural network (CNN) that processes 84x84 pixel images with 4-bit color depth, going through convolutions, ReLU functions, and ultimately choosing one of nine possible outputs (game actions) per frame.
8:29 Future Outlook: The speaker notes that photonic computing is in its "really early baby steps," facing challenges in scaling and integration. The future trajectory of optical computing, especially compared to quantum computing, remains uncertain, but it holds promise for ultra-low latency, low-power AI applications.