Knowm Inc., a start-up pioneering next-generation advanced computing architectures and technology, today announced the availability of two new variations of the Knowm memristor. The company also announced raw device data available for purchase that will greatly improve researchers’ ability to develop and improve memristor models. These new offerings facilitate significant advancements to be made in the field of neuromemristive processors, a new class of extremely efficient artificial intelligence (AI) learning processors.

“Memristors not only hold tremendous potential to advance digital computing, they also provide the unique physical properties needed to directly map learning and inference to physical circuits and create extremely efficient AI,” said Alex Nugent, CEO and co-founder of Knowm, Inc. “Our growing memristor portfolio and the available data will help to drive the industry forward, providing researchers with the tools they need to develop for this exciting new era of electronics and computing.”

Knowm’s two new memristors, as well as the company’s previously announced device, are now available in raw die (unpackaged) form with masks specifically designed for research probe stations, which reduces measurement issues that can be introduced in packaging, such as wire-bond contact resistance. Knowm’s memristors now come in three variants: Tungsten (W), Tin (Sn) and Chromium (Cr), which refers to the materials introduced in the active layer during fabrication. These devices have been designed for higher operating resistance and lower adaptation thresholds, properties desirable in low power computing applications. Each device has unique electrical properties, which allows circuit engineers to exploit these variations in their designs.

Knowm also announced the availability of raw memristor data for researchers and engineers. Currently electrical engineers looking to design new memristor circuits typically use idealized models that deviate substantially from physical reality. Having abundant raw data is the cornerstone of accurate model development, and accurate models are critical to integrated circuit design. Integrated electronics is expensive in part due to the cost of mask fabrication. If a circuit is built to exploit the properties of memristors, but the mathematical models used in simulations are not accurate, the chip will likely fail, resulting in the loss of millions of dollars. Knowm is the first to offer raw memristor data spanning multiple material variants.

“The potential of memristors is so huge that we are seeing exponential growth in the literature, a sort of gold rush as engineers race to design new circuits and re-envision old circuits,” added Nugent. “The problem is that in the race to publish, circuit designers are adopting models that do not adequately describe real devices. The only way to really fix this problem is to get the raw data out there and compete to develop and improve the mathematical models.”

For more information about Knowm, please visit Like Knowm on Facebook and follow Knowm on Twitter, Google+, and LinkedIn.

About Knowm Inc.

Knowm believes nature is the highest form of technology and has used that belief to solve a fundamental problem inherent in modern computing architectures when applied to machine learning. The company is developing AHaH Computing, a new form of computing that exploits memristors to radically reduce the energy of machine learning operations. Knowm is ushering in the next generation of neuromemristive computing hardware with the industry’s first general purpose neuromemristive processor specification (kT-RAM), software development platform (KnowmAPI), and the availability of memristor technology including discrete chips, raw dies, and Back End of Line (BEOL) CMOS wafer services. For more information about Knowm, please visit

Media Contact:
Marta Weissenborn
McGrath/Power Public Relations

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Business Wire

Knowm Drives Market Forward with Availability of Two New Memristors and Raw Device Data

Company Delivers Greater Choice and Flexibility for Memristor Technology and Enables the Creation of Improved Memristor Models

SANTA FE, N.M., January 20, 2016

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