54× faster inferencing than neural networks with logic-based AI models
Logic-based algorithms can achieve 52× lower energy usage than neural networks
Logic-based AI is naturally explainable, ensuring accountability for decisions made by the model
Reduce system costs by lowering compute complexity, inference costs, and bill of materials
Run low-power, AI models up to 250× faster. And do so on-server or on-device and on the edge. Enable new business cases and deployments across a whole range of problem domains.
hardware — no GPU, no accelerators
Benchmarked on a 32-bit ARM Cortex M7 STM32H7
While capable of handling complex machine learning tasks like neural networks, Literal Labs’ logical approach to AI algorithms offer a refreshing alternative—one that’s faster, more energy‐efficient, and naturally explainable.
Unlike neural networks, which are inspired by biology, our logic-based AI is rooted in propositional logic, reinforcement learning, and feedback loops. This makes it far more efficient, streamlining inference and reducing computational complexity, all while consuming significantly less energy.
We’re getting ready to open the gates. Soon, you’ll be able to train your own logic-based AI models using the very same tools our engineers use. Build, benchmark, and deploy forecasting models with zero code — and zero friction.
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