Run low-power, AI models up to 250x faster. And do so on-device and on the edge. Enable new business cases and deployments across a whole range of problem domains.
Tsetlin algorithms are less compute heavy than neural networks, and orders of magnitude lower energy usage per inference with acceleration
250X faster inferencing with Tsetlin machine models, and up to 1,000X when accelerated
Our technology uniquely enables edge training, without the need for cloud support
Our architecture enables explainability and ensures accountability for decisions made
While capable of handling complex machine learning tasks like neural networks, Tsetlin Machines offer a refreshing alternative—one that’s faster, more energy—efficient, and naturally explainable.
Unlike neural networks, which are inspired by biology, the Tsetlin approach is rooted in propositional logic. This makes it far more efficient, streamlining inference and reducing computational complexity, all while consuming significantly less energy.
