Tensordyne Converts AI Matrix Math To Logs To Crank Up Inference Oomph
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Tensordyne Converts AI Matrix Math To Logs To Crank Up Inference Oomph
Timothy Prickett Morgan
Timothy Prickett<br>Morgan
Co-Editor, Co-Founder, The Next Platform
Published<br>tue 16 Jun 2026 // 19:52 UTC
Right off the bat, let’s give a shout out to the mathematician propeller-heads who create the transformations that make it possible to do all kinds of high performance computing to simulate, model, and generate insight from massive amounts of noisy data.<br>Transformations are the key to such codes, and they rely on math that predates computing as we know it by centuries. There are all kinds of neat transformations.
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My personal favorite is the Fourier transform, which breaks down complex signals into their component sine and cosine waves and which are named for French Enlightenment-area mathematician and physicist Jean-Baptist Joseph Fourier, are a key component of both HPC and AI codes, of course, and are a nightmare to do with a pencil, let me tell you. (I could not do it today if you put a gun to my head, unless you gave me time to rifle through my bookshelf and read up. . . .)<br>A more recent transformation example is Google’s TurboQuant quantization, which shrinks the memory wall for AI inference. TurboQuant takes the input vectors for an AI workload, applies a random rotation matrix to the vectors, does scalar quantization of that data, and then rotates it back into vector space. (The math is more complex than this.) The upshot is that TurboQuant compresses the key-value cache by a factor of 6X. (I really need to dig into this Google innovation. Apologies for the delay.)<br>Tensordyne, one of the second generation of AI chip startups that is focused heavily on inference, has its own transformation twist at the center of its “Napier” AI inference engines, and like many good ideas, it sounds absolutely and perfectly obvious once you say it. Here is the crux of it: Multiplying matrices of numbers is hard, even for computers with matrix multiplier units. But adding banks of numbers is a lot easier. (Even your own neurons like addition more than multiplication.) And the key insight from Tensordyne’s founders is that if you convert data to logarithms, you can add them and avoid the multiplication overhead entirely. And, the Napier chip, named after Scottish mathematician, astronomer, and physicist John Napier of the late Renaissance, who invented logarithms and also was an early user of the decimal point and was therefore one of the first geeks to get us on the path to a floating point processing unit, does all of this conversion from matrix to logs all under the covers, invisibly.<br>Here’s the upshot: This shift to log math provides more than an order of magnitude better performance, lower price, and lower power consumption than hybrid architectures from Nvidia and Amazon Web Services. This is clearly something that AI inference needs, as the vast wealth that Nvidia has amassed so aptly demonstrates. As I am fond of saying, economic substitution is a law, it’s not just a good idea. If the Tensordyne architecture pans out and can support all of the big inference engines, and Tensordyne can get enough HBM to make them in high volume, this may be a DeepSeek moment for AI hardware.
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RK Anand, one of the co-founders of Tensordyne, tells The Next Platform that Broadcom is the chip shepherd for the Napier compute engine and that with Broadcom being the third largest buyer of HBM memory on the planet and the third largest buyer of chip wafers from Taiwan Semiconductor Manufacturing Co, supply shortages are not going to be any bigger of an issue for Tensordyne than it is for anyone else.<br>“As long as we are within lead times, we can get any volumes customers need,” Anand states emphatically.<br>With that, let’s learn a little about Tensordyne’s people and dive into its AI inference system architecture, which is code-named “Pareto” appropriately enough, given that the Pareto curves for AI inference are going to be a deciding factor in AI system purchases.<br>Jumping From Cars To Datacenters
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RK Anand and Gilles Backhus co-founded a company called Recogni back in September 2017, and the idea was to create AI inference systems for the automobile industry. Anand has a long history in Silicon Valley, and both co-founders are experts in signal processing, which comes as no surprise.<br>Anand, who is Tensordyne’s chief product officer, got his bachelor’s degree in electronics and communications engineering from Manipal Institute of Technology in 1988 and his master’s degree in computer engineering from Syracuse University in 1990. He spent six and a half years in the microprocessor division at Sun Microsystems after graduation as a senior engineering manager, and was the founding vice president of engineering when Juniper Networks was started in July 1996 to take...