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Maxim Karpov: How Enlightra’s Multi-Color Lasers Enable High-Bandwidth Data Communication for AI Clusters

One of the most powerful advantages of photonics comes from the fact that lasers with different wavelengths don’t interfere with each other.

Thus, if you want to parallelize data transmission, you don’t have to scale through physical hardware but can use multiple lasers with different colors, i.e., different wavelengths, in the same optical waveguide. 

Previously, building such a ‘multi-color’ laser required an entire optical table full of equipment. Since our last interview in the summer of 2022, Enlightra has made great progress building multi-color lasers on a silicon chip driven by a single continuous-wave laser. 

We had the pleasure of speaking again with their co-founder and co-CEO, Maxim Karpov, about what to keep in mind when developing silicon photonics, how they shifted focus from telecommunications to short-range data communication for AI clusters, and how the Intel Ignite startup program accelerated their journey: 

Future of Computing News

🤖 OpenAI Just Announced 4 New AI Features, and They’re Available Now (INC)

🤖💰 OpenAI and Anthropic Revenue Breakdown (Tanay’s Newsletter)

🤖 Liquid Foundation Models: Our First Series of Generative AI Models (Liquid)

🦾 Intel’s Falcon Shores Future Looks Bleak as It Concedes AI Training to GPU Rivals (HPCWire)

🦾 Even Gamma Rays Can’t Stop This Memory: Memristors self-heal even when struck by intense radiation (IEEE Spectrum)

🦾 US to award up to $100 million to boost use of AI for semiconductor materials (Reuters)

🦾 Japan to start building 1st 'zeta-class' supercomputer in 2025, 1,000 times more powerful than today's fastest machines (Live Science)

🧠 Brain-like Computers Tackle the Extreme: Edge Start-up BrainChip announces a new chip design for a milliwatt-level AI inference (IEEE Spectrum)

⚛️ Quantinuum eyes $10B IPO: What’s its potential impact on the quantum industry?

⚛️ Haiqu is looking to gain insights from people doing work related to variational algorithms for quantum computers. Your feedback is greatly appreciated—please fill out their survey.

Funding News

OpenAI just raised $6.6 billion to build ever-larger AI models (The Verge)

Artisan Raises $7.3M Seed Round (Artisan)

AI coding startup Poolside raises $500M from eBay, Nvidia, and others (TechCrunch)

Qodo raises $40M Series A to bring quality-first code generation and testing to the enterprise (TechCrunch)

Cerebras Files for an I.P.O., Trying to Take On Nvidia (New York Times)

Google to invest $1 billion in Thailand to build data center and accelerate AI growth (CNBC)

Fabless Semiconductor Startup FermionIC Design To Raise $6 Mn From Ashish Kacholia, Associates (Inc42)

Good Reads

How to train a model on 10k H100 GPUs? (Soumith Chintala)

“A quick note summarizing common knowledge among the large-scale training cohort”

Scaling Laws and Model Comparison: New Frontiers in Large-Scale Machine Learning (Marktechpost)

“The primary objective has evolved from preventing overfitting through regularization techniques to effectively scaling up models to consume vast amounts of data.”

Logic with light: Introducing diffraction casting, optical-based parallel computing (Phys Org)

“Essentially, optical computing leverages the speed of light waves and their ability to interact in complex ways with different optical materials without generating any heat.”

IBM’s Big Bet on the Quantum-Centric Supercomputer (IEEE Spectrum)

“While it has previously been called hybrid quantum-classical computing, and may go by other names, we call this vision quantum-centric supercomputing.”

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