Overcome the infrastructure strain of modern AI. Logic-based network (LBN) algorithms deliver fast, accurate, and explainable inference that runs on the smallest semiconductors. This isn’t optimisation — it’s a solution.
What are LBNs?Most AI today is built from numbers — vast, costly math sums that burn power and hardware alike. LBNs are built from logic. They run lean, explainable, and fast on existing silicon, no accelerators needed. What powers them is a series of technologies that make logic itself the engine of intelligence.
Minimal processing, maximum efficiency. By reducing computation to a single bit per operation, 1-bit processing slashes energy use and accelerates inference within each LBN. Proof that less can truly do more.
Raw data, reimagined. Data binarisation distills complex inputs into a clean, logical format, cutting memory overhead and boosting both speed and interpretability.
Data binarisation: Learn more
Each model is built on deterministic logic statements, ensuring decisions that are transparent, traceable, easily explained, and scalable.
Propositional logic: Learn more
Less does even more. Specialised sparsity techniques within LBNs remove computational redundancy, shrinking model size and power draw while preserving precision.
Instead of gradient descent, the Tsetlin Machines inside LBNs learn through rule-based logic, capturing patterns with remarkable efficiency. The outcome is AI that runs inference without the cost, complexity, or opacity of neural networks.
Tsetlin Machines: Learn more Why logic-based AI?Because scaling AI shouldn’t require scaling the planet. Logic-based AI runs on existing hardware and the chips we've likely already deployed. They cut energy use and deployment costs by up to 90%. They deliver power without the power plants.
These aren't deployment challenges
They're algorithm problems
Hardware can’t fix what algorithms waste. AI’s ambitions have outgrown its algorithms. Logic-based AI brings them back within reach.
Change the algorithm, and everything changes—speed. From speed to sustainability, every metric improves with logic-based AI. Here’s what that looks like.
Ultra-FastDelivers AI inference up to 250× faster. Real-time responses, no delays, no bottlenecks.
Power-FriendlyConsumes up to 52× less energy than neural networks—perfect for low-power, battery-driven devices.
AccurateAchieves ±2% of neural network accuracy while running leaner, faster, and with far greater efficiency.
Cost-EffectiveProcess data on-device. Cut cloud reliance, slash compute costs, and reduce data transfer overheads.
ReliableRuns on proven MCUs with local inference. Works anywhere — even in low-connectivity environments.
Ultra-ExplainableBuilt for explainable AI. Logic-based architecture ensures transparency, interpretability, and accountability.
Innovate FasterTransform IoT devices by embedding AI at the edge, unlocking new capabilities, smart features, and products.
Privacy FirstProcess data locally. Minimise risk. Keep insights secure—without ever compromising performance.
How to train LBNsEach model begins with data and ends with accuracy. Trained through Literal Labs’ platform, every LBN is tuned for its task — faster, explainable, and built to be deployed exactly where you need it.
Targeted hardware
Models are automatically compiled into optimised, low-level C++ code that aligns perfectly with your target hardware. Each translation is tuned for the characteristics of the DSP, MCU, or cloud platform it will run on, ensuring the lowest possible inference latency and energy consumption while maintaining complete compatibility with your deployment environment.
Training sweet spot
Every training produces thousands of logic-based models. And each is rigorously profiled against your hardware and operating constraints to uncover the perfect model with the optimal balance of speed, accuracy, and power efficiency. Literal Labs’ platform evaluates performance trade-offs through tens of thousands of tests, automatically configuring each deployment to achieve maximum throughput with minimal energy waste, no matter the deployment device or processor.
Edge-to-cloud scalability
From microcontrollers measured in kilobytes to CPU servers running enterprise workloads, the same logic-based model architecture scales effortlessly. LBNs’ training can be scaled to the available resources, retaining their efficiency, explainability, and accuracy whether deployed on a single embedded chip or on a Managed Inference Server instance. One architecture, every environment.
Retrain and redeploy
Models can evolve as your data changes. The platform allows parameters to be retrained and redeployed without the need for full recompilation, generating smaller, optimised update packages. This reduces complexity and bandwidth requirements, particularly for edge devices, allowing systems to stay current and high-performing with minimal downtime.
Validated benchmarks
Each LBN is verified through a remote hardware validation process that measures predictive performance, inference speed, and power efficiency on real-world devices. Automated benchmarking ensures that every model performs exactly as expected in production conditions, giving engineers and decision-makers a trusted, data-driven view of performance before deployment.
Versioning and drift control
Model versioning is built directly into the platform. It tracks performance metrics over time, monitors accuracy drift, and automatically triggers retraining when beneficial. This continuous feedback loop keeps deployed models reliable, efficient, and aligned with the evolving behaviour of your data, ensuring sustained performance and operational transparency.
Deploy anywhereAI fit, not force. It should make better use of the hardware that already exists.
LBNs run where intelligence already lives — on the 35 billion CPUs and MCUs shipping each year. They cut deployment and operating costs by up to 90 percent while consuming fifty times less energy. The result is AI that scales without new infrastructure, without new power demands, and without compromise.
When algorithms become logical, everything else becomes simple. In replacing inefficiency with logic, LBNs make AI commercially viable at scale.
Real-world impact
Helping hydro
Embedded hydro-informatics AI runs on battery sensors in sewers, monitoring harsh flows without cloud.
Well stocked, well fed
Inventory forecasts cut from 4 hours to 3 minutes, with 2× WMAPE accuracy across thousands of SKUs.
Fast cars
Edge AI for cars: logic-based models deployed in tight spaces where only PowerPC can run.
Anomalous performance
Benchmarked 52× faster than like-for-like best-in-class for ToyADMOS anomaly detection.
Battery life
LBNs cut power use so far a coin-cell runs 10 years, up from 3 months on an RNN.
Boosted boost
LBNs run up to 250× faster than XGBoost while using 130kB less memory.
LBNs start hereWe’re getting ready to open the gates. Soon, Literal Labs will be launching a training tool empowering you to train your own logic-based AI models with private or public datasets. Build, benchmark, and deploy models with zero code. And zero friction.
Enter your details below to be the first to know when our platform launches.
More speed
More energy efficient
More explainable
Literal Labs is a UK startup pioneering logic-based AI, whose Logic-Based Networks (LBNs) deliver up to 54× faster inference with around 52× less energy use than neural networks, running efficiently and sustainably on standard MCUs and CPUs without the need for GPUs.