Innovative company Knowm is making parts and services available, with a strong focus on machine learning. It offers a package – and evaluation chip – that contains eight Memristors. As shown in the graphic, in response to successive bidirectional applied voltage "write" pulses (black), the Memristor current (red) changes value progressively; the response is essentially symmetric in both (polarity) directions. Its behaviour as a hysteresis loop is shown in the second diagram.
The company says it is the first to develop and make commercially-available memristors with “bi-directional incremental learning” capability. The device was developed through research from Boise State University’s Dr. Kris Campbell. This has been previously believed to be impossible in filamentary devices by Knowm’s competitors despite significant investment in materials, research and development. Knowm claims the first commercial memristors that can adjust resistance in incremental steps in both direction rather than only one direction with an all-or-nothing ‘erase’. This advancement opens the gateway to extremely efficient and powerful machine learning and artificial intelligence applications.
“Having commercially-available memristors with bi-directional voltage-dependent incremental capability is a huge step forward for the field of machine learning and, particularly, AhaH [“Anti-Hebbian and Hebbian”, see Knowm’s explanation here] Computing,” said Alex Nugent, CEO and co-founder of Knowm. “We have been dreaming about this device and developing the theory for how to apply them to best maximize their potential for more than a decade, but the lack of capability confirmation had been holding us back. This data is truly a monumental technical milestone and it will serve as a springboard to catapult Knowm and AHaH Computing forward.”
The company continues, “Memristors with the bi-directional incremental resistance change property are the foundation for developing learning hardware such as Knowm Inc.’s recently announced Thermodynamic RAM (kT-RAM) and help realize the full potential of AHaH Computing. The availability of kT-RAM will have the largest impact in fields that require higher computational power for machine learning tasks