Knowm Inc was founded to develop memristive machine learning hardware and promote memristor science.
The Knowm memristor material stack is based on mobile metal ion conduction through a chalcogenide material. The resistance is related at any time to the amount of metal located within the active layer. Different doping materials in the active layer of the memristors lead to different physical and electrical differences such as switching speed, switching energy, endurance, data retention, on and off resistance states and the incremental sensitivity. Knowm Memristors are available for sale and can be shipped worldwide.
Anti-Hebbian and Hebbian computing is a new form of computing based on collections of differential-pair memristor synapses. It has been been shown to offer general solutions to memory, reconfigurable logic and machine learning.
kT-RAM provides a universal synaptic integration and adaptation substrate for AHaH Computing. The kT-RAM architecture allows for drastic reductions of energy associate with machine learning operations by reducing synaptic adaptation and inference to local analog operations.
The Knowm API is a software hook to kT-RAM, where machine learning functions have been reduced to kT-RAM instruction set routines.
The kT-RAM Technology Stack is a specification that goes from memristors to machine learning applications. This allows separate groups to specialize at one or more levels of the stack where their strengths and interests align. Improvements at various levels can propagate throughout the whole technology ecosystem, from materials to markets, without any single participant having to bridge the whole stack.