“We built machines that think like neurons. Now, with light, we build machines that learn with the speed of the universe.” – MJ Martin
Introduction: A Shift from Electrons to Photons
Advances in artificial intelligence are pushing conventional silicon computing to its limits. Modern AI models require immense computational throughput, rapid parallelism, and large-scale energy efficiency, yet traditional transistor-based chips struggle with heat dissipation, data-movement bottlenecks, and clock-speed ceilings. A new class of processors built on optical neural networks represents a major technological shift. Instead of relying on electrons travelling through silicon pathways, these chips use photons travelling at the speed of light through engineered photonic circuits. Companies such as Lightmatter, Lightelligence, and other research groups around the world are leading this move into a new era of photonic AI acceleration. The result is a computing architecture specifically designed for neural networks, able to outperform silicon in speed, efficiency, and scaling potential.
The Limits of Silicon and the Need for Alternatives
Silicon electronics have been the foundation of all modern computing for over half a century. However, the physics of electrons in silicon impose hard barriers. Transistors are now only a few nanometres in size, approaching quantum tunnelling limits. Shrinking them further increases leakage currents and heat, reducing efficiency and weakening the gains predicted by Moore’s Law. Beyond density, there is the problem of moving data. In neural networks, most time and energy is consumed not by arithmetic, but by shuttling data between memory and compute units. This bottleneck is magnified in large transformer models used in natural language processing, image synthesis, and scientific simulations. Even the most advanced GPUs spend significant energy simply waiting for data. The industry now recognizes that a radical shift in computing materials and architectures is required. Photonic computing offers such a shift.
What Is an Optical Neural Network?
An optical neural network is a computing architecture that uses light rather than electricity to execute the core mathematical operation of AI systems: matrix multiplication. Neural networks rely heavily on multiplying vectors and matrices during both training and inference. Optical systems can perform these operations in parallel by exploiting the natural physics of light interference. Instead of calculating each multiplication step individually as a digital system does, photonic circuits use the interaction of light waves to represent and combine values instantly.
The basic building block is the Mach–Zehnder interferometer, a device that splits and recombines beams of light. By controlling phase shifts within these interferometers, photonic circuits can encode numerical weights and execute the multiply-accumulate operations that define neural network layers. These operations occur as light passes through the circuit, enabling full-layer computations in a single propagation. Unlike electrical circuits that require a sequence of timed steps, optical circuits compute at the physical speed of light through their medium.
How Light-Driven Chips Work
Light-driven chips integrate several essential subsystems. First, lasers or micro-LEDs generate coherent light. This light flows into a photonic waveguide layer etched onto silicon or silicon nitride. Within this waveguide network, optical components perform the “analogue math” of neural networks. The intensity and phase of each optical signal represent numerical values in the network. Once the computation is complete, photodetectors convert the optical outputs back into electrical signals for interfacing with memory or downstream processors.
Many photonic chips operate in hybrid form. The optical domain performs matrix multiplication, while traditional electronics handle nonlinear activation functions, memory access, and control flow. This hybrid architecture allows breakthrough speed and efficiency without requiring the entire computing stack to become optical overnight. Lightmatter’s Envise system and Lightelligence’s Pace accelerator are examples of such hybrid photonic-electronic platforms.
A key advantage is that optical interference is inherently parallel. Light beams do not interact unless designed to, and multiple wavelengths can travel through the same waveguide simultaneously. This enables a single photonic circuit to run many calculations at once, achieving massive parallelism without the heat and resistance that limit electrical signals.
Speed: How Fast Are Optical Neural Chips?
Optical processors do not operate at the “speed of light” in a vacuum, but rather at the speed at which photons travel through silicon photonic waveguides. Even so, this is orders of magnitude faster than the drift velocity of electrons in silicon. More importantly, optical processors are not constrained by clock cycles. A matrix multiplication that may require billions of sequential electronic operations can be executed in a single pass of light through the circuit.
In practical benchmarks, photonic AI accelerators have demonstrated performance equivalent to hundreds of TOPS (trillions of operations per second) at a fraction of the latency of GPUs. Lightmatter has stated that its photonic engines can accelerate AI workloads by five to ten times compared to leading GPUs in certain dense linear-algebra tasks. Theoretical performance ceilings are even higher because optical systems scale by increasing wavelengths and waveguide density rather than shrinking transistors.
While today’s products are still early in commercialization, the physics of optical interference suggest long-term scaling far beyond what silicon can provide.
Energy Efficiency and Power Consumption
One of the most profound advantages of light-based computing is energy efficiency. Traditional silicon chips require substantial power to move electrons and charge capacitors. GPUs often consume hundreds of watts per chip, and large AI training clusters draw megawatts. Optical neural networks eliminate resistive losses because photons do not generate heat in the same way electrons do. Lightmatter and other leaders in the field report up to ten-fold reductions in power consumption for equivalent matrix-multiplication workloads.
This is achieved through three mechanisms. First, optical interference performs many operations “for free,” using the physics of light rather than electrical switching. Second, optical signals do not require repeated recharging of capacitors, which is one of the largest contributors to energy consumption in digital logic. Third, the inherent parallelism of photonics allows fewer processing steps, reducing energy per operation.
The result is a class of chips well-suited for AI inference at scale, cloud-data-centre efficiency, and edge devices where power budgets are limited.
Applications for AI and Beyond
The initial focus of optical neural networks is accelerating deep-learning workloads. Large language models, computer vision systems, recommendation engines, and scientific AI simulations all rely heavily on matrix operations that photonics handle efficiently. Data centres deploying AI-as-a-service are likely among the earliest adopters because of the strong economic incentives to cut energy costs and increase throughput.
Beyond AI, photonic processors show promise for quantum-inspired algorithms, high-speed signal processing, autonomous vehicles, advanced sensing, and real-time robotics. Optical chips may also complement quantum computers by handling classical pre- and post-processing at extremely high speeds.
Summary: A New Era Beyond Silicon
The transition from electron-driven to light-driven computing represents a fundamental evolution in how machines process information. Optical neural networks break free of the physical limits of silicon, offering higher speeds, lower power consumption, and architectural scalability aligned with the demands of next-generation AI. While still early in their commercial adoption, photonic chips from innovators such as Lightmatter signal the beginning of a new computing paradigm. As AI models continue to grow, and as society demands faster, more efficient computation, light-based processors are poised to become a central pillar of the future computing landscape.
About the Author:
Michael Martin is the Vice President of Technology with Metercor Inc., a Smart Meter, IoT, and Smart City systems integrator based in Canada. He has more than 40 years of experience in systems design for applications that use broadband networks, optical fibre, wireless, and digital communications technologies. He is a business and technology consultant. He was a senior executive consultant for 15 years with IBM, where he worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX).
Martin served on the Board of Directors for TeraGo Inc (TGO: TSX) and on the Board of Directors for Avante Logixx Inc. (XX: TSX.V). He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology. He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now Ontario Tech University] and on the Board of Advisers of five different Colleges in Ontario – Centennial College, Humber College, George Brown College, Durham College, Ryerson Polytechnic University [now Toronto Metropolitan University]. For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section.
He holds three master’s degrees, in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and seven certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has completed over 60 next generation MOOC (Massive Open Online Courses) continuous education in a wide variety of topics, including: Economics, Python Programming, Internet of Things, Cloud, Artificial Intelligence and Cognitive systems, Blockchain, Agile, Big Data, Design Thinking, Security, Indigenous Canada awareness, and more.