Optical Chips for
AI Computing

To deliver the world’s first
optical supercomputer with
of 100x energy efficiency

Get Started

About Us

Light powered AI Accelerated Computing

Scaling CMOS-based processors is increasingly challenging due to the slowdown of Moore's Law and the breakdown of Dennard scaling; data movement with electronic wires (capacitive loss) limit clock speeds and lead to thermal dissipation.

Opticore patented optical processing units (OPUs) are designed to handle memory-intensive AI tasks with on-chip data movement and computation with photonic integrated circuits. Our prototype achieves 100x higher energy efficiency and 100x greater computing density than digital electronics. Fully integrated with foundry-based, co-packaged optoelectronics, the system promises to lead breakthroughs in the next era of high performance computing in data centers.

Light Powers Accelerated Computing

Challenges

Opticore Product Deliverables

Parameter

Value

Throughput
Power
Chip area
Memory bandwidth
AI Mode scalability

1,000 POPS (single chip)
5 KW
1560 mm2
480 TB/s (300 HBMs)
Trillion matrix elements

AI Tasks are memory intensive

Opticore Technology

The photonic
logic - Photoelectric
Multiplication

Opticore team proposed (PRX 9, 2019) and demonstrated (Nat. Photonics 17, 723–730) the first photonic logic based on photoelectric multiplication in coherent detection, which is the fundamental building block  to enable large-scale, low energy computing with O(N) devices for O(N2) computes per clock cycle. As photonic technologies have become a necessity in datacenters for chip-to-chip communications, computing in the optical domain is an ultimate solution to overcome the electronic bottlenecks in energy efficiency and computing density with minimum change to the existing data center infrastructure.

Overcoming the memory wall: Less wires, more computations.

The “memory wall” in AI computing stems from the capacitive resistance of metallic wires used for memory data movement, which limit clock speeds  (1-3 GS/s) and lead to energy consumption. Opticore OPUs utilize the same high-bandwidth memories (HBMs) for their scalability and performance, but here the OPUs convert memory data to optical beams for on-chip movements with waveguides and computations with optical devices. Without capacitive resistance in optical waveguides, memory no longer needs to be physically close to processors, enabling potentially unlimited memory capacities.

Towards Trillion Neural Parameters with Temporal Mapping

Photonic devices are significantly larger than electronic transistors, which makes scaling existing optical computing approaches that map neural parameters onto photonic hardware (1 device/parameter) challenging. Opticore addresses this limitation by leveraging innovations in temporal mapping, where neural data is encoded using optical pulses. For example, an optical modulator can activate tens of billions of parameters per second, and through parallel channels, trillions of parameters can be processed simultaneously. Furthermore, all parameters can be dynamically programmed for training tasks, enabling highly flexible and scalable computing.

Volume production with CMOS foundry process

The Opticore computing chips are fabricated with standard foundry services, with co-packed optolelectronics and hyperbonding of HBMs.

Our Teams

Founders and Strategic Advisors

Alex Turnbull
Director

Alex Turnbull

Alex was CIO at Keshik Capital, a multi-strategy fund based in Singapore and now runs a family office. Previously, Alex worked at leading global investment banks and hedge funds. He has close to two decades of experience managing multi-asset portfolios and investing in public and private corporate securities and derivatives. Alex also conducts research on energy and security with the Australian National University and has published in top journals. Alex has a degree in economics, and he speaks English and Mandarin.

Dirk Englund
Advisor

Dirk Englund

ECE Professor at MIT,

PhD in Applied Physics, Stanford,

B.S. Caltech,

Awards: Presidential Early Career Award, Humboldt Professorship, DARPA Young Faculty Award.

Co-founder of DUST Identity (Security company), QuEra Computing (quantum control), Advisor of Lightmatter (optical computing)

First demonstration of integrated photonics for deep learning,  spinning off Lightmatter and Lightelligence

Chun-ho Lee
Scientist, Chip design & fabrication

Chun-ho Lee

Postdoc, UC Berkeley

PhD, KAIST (Korea)

Bachelor: Seoul National University

Skills: photonics and microwave simulation, chip layout designs, and fabrication

Kenaish Al Qubaisi
Chip Designer

Kenaish Al Qubaisi

MIT Postdoc fellow,

PhD, Electrical Engineering, Boston University

Awards: Charles L. Newton Prize, John F. Flagg Prize, for outstanding students. Wilder Trustee Scholarship, Xerox Engineering Research fellowship

Adam Cai
Business development

Adam Cai

Adam was the Partnership Development Manager and Tax Advisor at Carta, a leading capitalization table management company backed by prominent investors such as Andreessen Horowitz, Tribe Capital, and Union Square Ventures. Prior to Carta, Adam honed his expertise at EY, where he played a pivotal role in scaling multiple tax startups and practices to achieve seven-figure ARR after the first year of joining. Recognized for his innovative approach, Adam has been featured in Berkeley Haas News and a16z articles for his outreach and impact. He is a proud graduate of UC Berkeley’s Haas School of Business.

Mengjie Yu
Co-founder

Mengjie Yu

Assistant Professor, EECS, UC Berkeley

Postdoc, Harvard University

PhD, Cornell University & Columbia University

Awards: 2023 DARPA Young Faculty Award, 2023 Optica Foundation Award, 2020 The Optical Society (OSA) Ambassador, 2019 The Rising Stars, 2019 Caltech’s 2019 Young Investigator Lecturer, 2016 Maiman Student Paper Competition at CLEO, 2016 Emil Wolf Student Paper Competition

Zaijun Chen
Co-founder

Zaijun Chen

Assistant Professor, EECS, UC Berkeley

Postdoc researcher, MIT

PhD, Max-Planck Institute-Quantum Optics,

Awards: 2023 DARPA NaPSAC, 2024 DARPA INSPIRED, Sony Faculty Research Award, 2023 SPIE AI/ML best paper Award, 2023 Optica foundation award

Demonstration of the Opticore computing with 100x more energy efficiency, Nat. Photonics 2023

Ryan Hamerly
Co-founder

Ryan Hamerly

Research Scientist, MIT

Senior Scientist: NTT PHI lab

PhD, Stanford University

Bachelor: Caltech

Awards: Intelligence Community fellowship

Theory proposal and analysis for the OptiCore computing architecture, R. Hamerly, PRX 2019

Latest News

Related Scientific Articles

Large-Scale Optical Neural Networks Based on Photoelectric Multiplication

Large-Scale Optical Neural Networks Based on Photoelectric Multiplication

Asymptotically fault-tolerant programmable photonics

Asymptotically fault-tolerant programmable photonics

Deep learning with coherent VCSEL neural networks

Deep learning with coherent VCSEL neural networks

Delocalized photonic deep learning on the internet’s edge

Delocalized photonic deep learning on the internet’s edge

Single-shot optical neural network

Single-shot optical neural network

Lithium niobate photonics: Unlocking the electromagnetic spectrum

Lithium niobate photonics: Unlocking the electromagnetic spectrum

Hurdling the AI Power Wall With Photonic Computing

Hurdling the AI Power Wall With Photonic Computing

CAReer OPPortunities

Join the Vision

Team

Role

Location

Development

Optical Engineering

Fremont (CA)/Remote

Development

Photonic Systems

Fremont (CA)/Remote

Development

IC Design

Fremont (CA)/Remote

Machine-learning system based on light could yield more powerful, efficient large language models

- MIT news

Find Out More

Machine-learning system based on light could yield more powerful, efficient large language models

- MIT news

Find Out More