Phillip Burr is the Head of Product at Lumai, with over 25 years of expertise in international product administration, go-to-market and management roles inside main semiconductor and expertise corporations, and a confirmed observe document of constructing and scaling services and products.
Lumai is a UK-based deep tech firm creating 3D optical computing processors to speed up synthetic intelligence workloads. By performing matrix-vector multiplications utilizing beams of sunshine in three dimensions, their expertise gives as much as 50x the efficiency and 90% much less energy consumption in comparison with conventional silicon-based accelerators. This makes it significantly well-suited for AI inference duties, together with giant language fashions, whereas considerably decreasing vitality prices and environmental impression.
What impressed the founding of Lumai, and the way did the thought evolve from College of Oxford analysis right into a industrial enterprise?
The preliminary spark was ignited when one of many founders of Lumai, Dr. Xianxin Guo, was awarded an 1851 Analysis Fellowship on the College of Oxford. The interviewers understood the potential for optical computing and requested whether or not Xianxin would take into account patents and spinning out an organization if his analysis was profitable. This obtained Xianxin’s artistic thoughts firing and when he, alongside one in every of Lumai’s different co-founders Dr. James Spall, had confirmed that utilizing gentle to do the computation on the coronary heart of AI might each dramatically increase AI efficiency and cut back the vitality, the stage was set. They knew that current silicon-only AI {hardware} was (and nonetheless is) struggling to extend efficiency with out considerably growing energy and price and, therefore, if they may clear up this drawback utilizing optical compute, they may create a product that clients needed. They took this concept to some VCs who backed them to type Lumai. Lumai not too long ago closed its second spherical of funding, elevating over $10m, and bringing in further buyers who additionally consider that optical compute can proceed to scale and meet growing AI efficiency demand with out growing energy.
You’ve had a powerful profession throughout Arm, indie Semiconductor, and extra — what drew you to hitch Lumai at this stage?
The quick reply is group and expertise. Lumai has a powerful group of optical, machine studying and information heart consultants, bringing in expertise from the likes of Meta, Intel, Altera, Maxeler, Seagate and IBM (together with my very own expertise in Arm, indie, Mentor Graphics and Motorola). I knew {that a} group of exceptional individuals so centered on fixing the problem of slashing the price of AI inference might do wonderful issues.
I firmly consider that way forward for AI calls for new, revolutionary breakthroughs in computing. The promise of with the ability to supply 50x the AI compute efficiency in addition to reducing the price of AI inference to 1/tenth in comparison with as we speak’s options was simply too good a chance to overlook.
What had been among the early technical or enterprise challenges your founding group confronted in scaling from a analysis breakthrough to a product-ready firm?
The analysis breakthrough proved that optics may very well be used for quick and really environment friendly matrix-vector multiplication. Regardless of the technical breakthroughs, the most important problem was convincing folks that Lumai might succeed the place different optical computing startups had failed. We needed to spend time explaining that Lumai’s method was very completely different and that as a substitute of counting on a single 2D chip, we used 3D optics to succeed in the degrees of scale and effectivity. There are after all many steps to get from lab analysis to expertise that may be deployed at scale in a knowledge heart. We acknowledged very early on that the important thing to success was bringing in engineers who’ve expertise in creating merchandise in excessive quantity and in information facilities. The opposite space is software program – it’s important that the usual AI frameworks and fashions can profit from Lumai’s processor, and that we offer the instruments and frameworks to make this as seamless as doable for AI software program engineers.
Lumai’s expertise is alleged to make use of 3D optical matrix-vector multiplication. Are you able to break that down in easy phrases for a basic viewers?
AI techniques must do a variety of mathematical calculations referred to as matrix-vector multiplication. These calculations are the engine that powers AI responses. At Lumai, we do that utilizing gentle as a substitute of electrical energy. This is the way it works:
- We encode info into beams of sunshine
- These gentle beams journey by 3D house
- The sunshine interacts with lenses and particular supplies
- These interactions full the mathematical operation
Through the use of all three dimensions of house, we are able to course of extra info with every beam of sunshine. This makes our method very environment friendly – decreasing the vitality, time and price wanted to run AI techniques.
What are the principle benefits of optical computing over conventional silicon-based GPUs and even built-in photonics?
As a result of the speed of development in silicon expertise has considerably slowed, every step up in efficiency of a silicon-only AI processor (like a GPU) ends in a big enhance in energy. Silicon-only options eat an unimaginable quantity of energy and are chasing diminishing returns, which makes them extremely advanced and costly. The benefit of utilizing optics is that after within the optical area there’s virtually no energy being consumed. Vitality is used to get into the optical area however, for instance, in Lumai’s processor we are able to obtain over 1,000 computation operations for every beam of sunshine, each single cycle, thus making it very environment friendly. This scalability can’t be achieved utilizing built-in photonics on account of each bodily measurement constraints and sign noise, with the variety of computation operations of silicon-photonic answer at solely at 1/eighth of what Lumai can obtain as we speak.
How does Lumai’s processor obtain near-zero latency inference, and why is that such a essential issue for contemporary AI workloads?
Though we wouldn’t declare that the Lumai processor gives zero-latency, it does execute a really giant (1024 x 1024) matrix vector operation in a single cycle. Silicon-only options sometimes divide up a matrix into smaller matrices, that are individually processed step-by-step after which the outcomes should be mixed. This takes time and ends in extra reminiscence and vitality getting used. Decreasing the time, vitality and price of AI processing is essential to each permitting extra companies to profit from AI and for enabling superior AI in essentially the most sustainable means.
Are you able to stroll us by how your PCIe-compatible type issue integrates with current information heart infrastructure?
The Lumai processor makes use of PCIe type issue playing cards alongside a normal CPU, all inside a normal 4U shelf. We’re working with a spread of knowledge heart rack tools suppliers in order that the Lumai processor integrates with their very own tools. We use customary community interfaces, customary software program, and so on. in order that externally the Lumai processor will simply seem like another information heart processor.
Information heart vitality utilization is a rising international concern. How does Lumai place itself as a sustainable answer for AI compute?
Information heart vitality consumption is growing at an alarming fee. In response to a report from the Lawrence Berkeley Nationwide Laboratory, information heart energy use within the U.S. is anticipated to triple by 2028, consuming as much as 12% of the nation’s energy. Some information heart operators are considering putting in nucleus energy to supply the vitality wanted. The trade wants to take a look at completely different approaches to AI, and we consider that optics is the reply to this vitality disaster.
Are you able to clarify how Lumai’s structure avoids the scalability bottlenecks of present silicon and photonic approaches?
The efficiency of the primary Lumai processor is simply the beginning of what’s achievable. We count on that our answer will proceed to supply large leaps in efficiency: by growing optical clock speeds and vector widths, all and not using a corresponding enhance in vitality consumed. No different answer can obtain this. Customary digital silicon-only approaches will proceed to eat increasingly more value and energy for each enhance in efficiency. Silicon photonics can not obtain the vector width wanted and therefore corporations who had been taking a look at built-in photonics for information heart compute have moved to deal with different components of the information heart – for instance, optical interconnect or optical switching.
What position do you see optical computing enjoying in the way forward for AI — and extra broadly, in computing as a complete?
Optics as a complete will play an enormous half in information facilities going ahead – from optical interconnect, optical networking, optical switching and naturally optical AI processing. The calls for that AI is inserting on the information heart is the important thing driver of this transfer to optical. Optical interconnect will allow sooner connections between AI processors, which is crucial for giant AI fashions. Optical switching will allow extra environment friendly networking, and optical compute will allow sooner, extra power-efficient and lower-cost AI processing. Collectively they are going to assist allow much more superior AI, overcoming the challenges of the slowdown in silicon scaling on the compute facet and the pace limitations of copper on the interconnect facet.
Thanks for the good interview, readers who want to be taught extra ought to go to Lumai.
