The topic of light processors seems to be gathering a lot of interest recently.
The following article is a good introduction of the current state of play, Build a Better AI Supercomputer.
In addition this earlier article gives a more detailed introduction, Building Light into Chips and this article Lightmatter Passage Brings Optics and Silicon Photonics to the Chiplet Era which is a more technical article.
The following is my brief introduction to Photonic Processors and Lightmatter
Introduction
Artificial intelligence (AI) is experiencing an unprecedented surge in development, driven by huge investments from tech giants like Google, Microsoft, Amazon and Facebook. However, escalating complexity of AI models has outstripped capabilities of traditional silicon processors. Lightmatter, a startup founded by three MIT alumni, proposes a groundbreaking solution: integrate photonic processors with traditional silicon chips. Its goal is nothing less than revolutionizing AI computing.
The Challenges of AI Computing
The exponential growth of AI demands increasingly powerful computing. Traditional silicon processors, the bedrock of computing for decades, are being pushed to their limits. Moore’s Law, the tongue-in-cheek rule that has long posted on the walls of computing departments, which meant the best performance doubling every two years, has been faltering.
Overheating due to increased transistor density, along with communication costs and electrical signal resistance hampers efficiency and scalability in today’s AI systems.
The Promise of Photonic Processors
LightMatter seeks to take advantage of the strengths of light-based processing combined with traditional silicon chips. Photonic processors, which use photons in place of electrons, side-step resistance and heat generation to produce both faster processing and better efficiency of energy.
The Passage Chip Interconnect: Enabling Faster Communication
A breakthrough innovation by Lightmatter, the Passage chip interconnect takes advantage of photonic technology so that chip-to- chip communication becomes both quick and efficient. This interconnect makes use of optical signals to overcome limitations in electrical signals such as resistance and heat generation. Therefore, it supports high-bandwidth, low-latency communication between chips, enabling real-time feedback and speeding up AI computations.
Envise: A General-Purpose Cloud Inference Accelerator
Envise, the second product of Lightmatter, is a flexible cloud inference accelerator. It combines computation elements with a pace-setting photonic computing core. By using the speed of light Envise can bring latency down markedly, this makes rapid computations and real-time AI applications possible.
The Potential Impact of Lightmatter’s Technology
Lightmatter’s photonic processors promise to revolutionize the world of AI computing, providing faster processing speeds, utilizing energy more efficiently and enjoying enhanced scalability. Lightmatter aims to combine photonic processors with traditional silicon chips. In this way it seeks to address the problems before the AI industry. This technology has the possibility to make major leaps forward in AI computing, benefiting many industries and applications.
Conclusion
Lightmatter’s photonic processors represent an innovative model of AI computing. Lightmatter aims to overcome the current constraints on AI models of large-scale by integrating traditional silicon processors with photonic technology. With faster processing speeds greater energy efficiency and enhanced scalability, Lightmatter’s technology is expected to define the future of AI computing.