Data Binarisation
Binarisation converts continuous data into simple 0 or 1 values. That reduction in complexity is the foundation for faster computation, smaller models, and more interpretable AI.
But it comes with trade-offs. Understanding when binarisation helps and when it hurts determines whether it is the right technique for a given task.
Simplifying data to its essentials.
Data binarisation is a data transformation technique that converts continuous numerical data into a binary format — typically 0 or 1. Rather than preserving a measurement's full precision, it asks a simpler question: is this value above or below a threshold?
That simplification has a direct effect on computation. Binary values require less storage. Operations on binary representations are faster and cheaper than their floating-point equivalents. And for AI models that are designed from the ground up to work with binary inputs, that reduction in data complexity translates into faster training, faster inference, and lower energy consumption.
Binarisation is most naturally applied to numerical data — time series, sensor readings, anomaly signals. Literal Labs' AI models extend this further, enabling non-numerical data including sounds and images to be represented in binary before being passed to a Tsetlin machine for further processing.
Where the technique performs well.
Machine health monitoring
Audio sensors continuously monitor the sound emitted by a machine. By applying binarisation, an AI model sets a threshold for normal operational noise levels. Any sound pattern that deviates from this expected range — grinding, screeching, or sudden changes in frequency — is classified as an anomaly (1), while regular sound levels are marked as normal (0). This binary distinction enables AI models to flag abnormal machine behaviour quickly, as confirmed by successful benchmarking on ToyADMOS.
Predictive maintenance
Machines with moving parts generate specific vibration patterns during normal operation. As components wear out or faults develop, those vibration signatures change. An AI model trained with binarisation learns the appropriate threshold for normal vibration from historical data. Any vibration amplitude above that level is marked as abnormal (1), those within acceptable range as normal (0) — enabling automatic detection of potential issues and scheduled maintenance before failure occurs.
What binarisation does well.
Simplified data
Reducing complex, continuous data to 0 or 1 values makes it easier for AI models to process and eliminates noise introduced by high-precision representations that do not contribute to classification accuracy.
Faster computation
With reduced data complexity, models perform faster calculations and require less computational power. On hardware without a floating-point unit, the speed advantage is especially pronounced — bitwise operations execute in a single clock cycle where floating-point operations require emulation.
Enhanced interpretability
Binary data is more straightforward by nature, and part of what helps make LBNS explainable AI models. When a model reasons in terms of true/false conditions rather than decimal weights, its logic is more accessible to engineers and auditors.
Efficient storage
Binary data takes up less space, resulting in smaller models, more efficient storage, and quicker data retrieval. For edge deployments where memory is constrained, that reduction in model footprint can determine whether deployment is feasible at all.
Where the trade-offs appear.
Threshold sensitivity
Models depend heavily on the chosen threshold. Incorrect threshold selection can lead to inaccurate results — values that should be classified as anomalous may be missed, or normal values incorrectly flagged.
Limited application scope
Binarisation is less effective for tasks that require nuanced or continuous data, such as regression problems. Where the output must be a precise value rather than a category, the binary approach is not the right fit.
Reduced flexibility
Binary data can oversimplify complex relationships, limiting a model's ability to capture intricate patterns. Not every classification problem maps cleanly onto binary conditions.
Edge-case misclassification
Cases that fall close to the threshold may be misclassified, leading to false positives or negatives. Threshold selection and validation against real-world data is essential to keeping this error rate acceptable.
Accuracy without the trade-off.
The conventional view in machine learning is that binarisation reduces accuracy. Literal Labs' approach challenges that assumption.
When data binarisation is used in parallel with a series of complementary machine learning techniques — as it is inside Logic-Based Networks — the accuracy impact is minimal. Benchmarking has shown average accuracy variation of ±2% compared to floating-point baselines. In many cases, LBNs built on binarised data are not just faster and more energy-efficient than their neural network counterparts — they are also more accurate.
That result is not accidental. It reflects a pipeline designed around binarisation from the ground up, rather than one that applies it as an afterthought to a floating-point model.
Put binarisation to work.
Literal Labs’ AI pipeline applies data binarisation alongside Logic-Based Network training to build models that are fast, efficient, and accurate. If you’d like to understand how to use the technique for your own data, get in touch.