1-Bit Processing

Binary computation, from the ground up

In conventional AI inference, values are represented as floating-point numbers — a weight might be 0.347, an activation might be −1.892. The model's prediction emerges from thousands of such multiplications and additions applied in sequence.

1-bit processing represents values as single bits: 0 or 1. The operations that combine them — AND, OR, NOT, XNOR — are the elementary operations of digital logic itself. Any processor — from a server CPU to a sub-1MHz microcontroller — handles these operations natively and at maximum speed.

The critical distinction is that 1-bit processing in the context of Logic-Based Networks is not quantisation. Quantisation takes a neural network trained on floating-point values and rounds its weights to lower precision. Logic-Based Networks are trained from the ground up in a binary representation. Data binarisation converts raw sensor inputs into binary features before training begins. The model learns to classify on binary inputs using binary logic. There is no floating-point precision being discarded because there was never floating-point precision to begin with.

Single-cycle operations, no specialised hardware required

Consider what happens when a processor executes a 32-bit floating-point multiply. It requires a dedicated floating-point unit (FPU), multiple clock cycles, and substantially more energy than an integer operation of equivalent precision. Many embedded processors — particularly lower-cost Arm Cortex-M0, M0+, and M1 cores — have no FPU at all. Floating-point operations on these devices are emulated in software, making neural network inference on them not merely slow, but impractical.

A bitwise AND or OR operation, by contrast, executes in a single clock cycle on every processor in common use. There is no dedicated hardware required, no emulation fallback, no precision-dependent penalty.

For 1-bit AI inference, an entire clause — a logical rule combining multiple conditions — can be evaluated in a handful of clock cycles. An inference that would require thousands of floating-point operations on a neural network can be completed with a small number of bitwise operations on an LBN.

This is why the benchmark numbers are what they are: 54× faster inference, 52× less energy per inference compared to equivalent neural networks, as measured by MLPerf Tiny. The gap is not surprising when you trace it back to the hardware arithmetic. It is a predictable consequence of computation type.

Real-world classification through logical rules

The reason 1-bit processing is viable as an AI technique — rather than merely a compression hack — is that real-world classification problems can be expressed in propositional logic without meaningful loss of accuracy.

Propositional logic allows a model to express rules of the form: "if condition A is true AND condition B is true AND condition C is false, then output is class X." These are not approximations of some underlying floating-point truth. They are the actual learned patterns in the data, expressed in a form that maps directly to binary computation.

Logic-Based Networks learn these clauses through a reinforcement-based training process. Clauses that predict correctly are reinforced; clauses that predict incorrectly are suppressed. Over training iterations, the network converges on a set of logical rules that characterise the classes in the training data.

The output is a model composed entirely of propositional clauses. Inference is the evaluation of those clauses against a binarised input. The computation is bitwise throughout — from input preprocessing to output classification.

Different in kind, not degree

Binary neural networks (BNNs) are neural networks in which weights and/or activations are constrained to ±1 values — a form of extreme quantisation applied to a neural network architecture. BNNs still use a neural network training procedure (backpropagation), still produce a weight-based model, and still require a neural network inference runtime. The 1-bit constraint improves efficiency relative to full-precision networks, but BNNs retain the fundamental architectural properties of neural networks: opacity, sensitivity to weight precision, and dependence on the network's layer structure.

LBN 1-bit processing is different in kind, not degree. The model is not a neural network with binary weights; it is a logical structure that was never a neural network. The training procedure is different, the model representation is different, and the inference procedure is different. The term "1-bit processing" in the LBN context refers to the full-stack binary nature of the computation: binary inputs, binary clauses, binary outputs, bitwise evaluation.

The distinction matters practically. BNNs still require a neural network deployment stack and still carry the interpretability limitations of neural architectures. LBN-based 1-bit processing produces an inherently explainable model with no neural network infrastructure required.