Modern AI runs on floating point — billions of approximate multiplications. We train neural networks whose weights are small whole numbers, so inference is integer addition and bit-shifts. The models are radically smaller, run on almost anything — and because whole-number arithmetic is exact, a result can be replayed bit for bit, anywhere.
Each BitBrain model is trained from scratch with our proprietary pipeline, on whole-number weight alphabets tuned to the task. We train the model. You license and deploy it.
Streaming BitBrain models for real-time audio analysis and manipulation: speech noise suppression, acoustic echo cancellation, keyword spotting. State is kilobytes and the cost per frame is constant, so they run continuously on ultra-low-power devices — no multiplier hardware needed for the core arithmetic.
Lightweight whole-number vision models for object detection, recognition, and automated inspection — accurate without a float multiplier in sight, small enough for continuous camera processing on the device itself.
Language and time-series models in two complementary modes: full-context models for writing and understanding, and streaming models whose cost per step is constant. Both run on the same whole-number arithmetic, and both are small enough to run client-side.
Our training pipeline is model-agnostic. If a neural network can solve your problem, we can build a BitBrain version of it — dramatically smaller, deployable on your target hardware.
Public benchmarks where they exist, published human baselines where they exist — and the caveats stay attached. Live demonstrations of every model family are available on request.
Google Speech Commands v2, official test set — above the published floating-point reference model (94.4%), in a model that fits in a microcontroller's spare memory.
Standard VoiceBank-DEMAND speech-quality benchmark, protocol-strict. Best in class at this size and power budget — large float research systems score higher and we don't claim otherwise; they don't fit where ours runs.
A 398 KB detector finding road damage from a moving vehicle camera — no GPU anywhere in the pipeline. Proof of concept.
On a published clinical benchmark's reader study, our sealed-test model scored above the average of the eight dermatologists on the same 100 images. A narrow, research-only claim — not a medical device — but the evaluation discipline behind it is the product.
A complete model reproduced in SPICE circuit simulation, 20/20 test cases in agreement — and simulated analog memory hardware drifted just −0.16% over a year. The arithmetic is already at home in physics.
Our latest language models run the entire stack — attention and normalisation included — without a single multiplication, counted live by the browser's own op counter: billions of adds and shifts per paragraph, zero multiplies.
Extreme quantisation should destroy model accuracy. Ours doesn't. A proprietary training pipeline makes the difference.
BitBrain models aren't compressed after training — they're trained quantised from the first step, on weight alphabets of small whole numbers: as few as three states {-1, 0, +1}, at most five {-2 … +2}. Every forward pass is integer addition and bit-shifts; our latest language models run the entire stack without a single multiplication.
Quantisation introduces systematic errors that compound through layers. We developed a proprietary correction mechanism that operates during training — continuously detecting and compensating for quantisation noise without adding inference cost. The result: accuracy that standard quantisation can't match.
Full-context models give language tasks deep understanding of everything in the window. Streaming models process unbounded live signals — audio frames, camera feeds, telemetry — at constant cost per step, with state measured in kilobytes. Same whole-number arithmetic, same export pipeline; we pick the mode per task, and some products use both.
Models export to a single portable file. Inference is pure integer arithmetic — runs in any browser, on any microcontroller, in any language. No framework dependencies. No GPU. No runtime. The same model runs on a cloud server and a hearing aid.
Whole-number addition is exact, so in deployment form a BitBrain model produces the same bits on any hardware, any thread count, any platform — and every output can carry a cryptographic replay receipt. Re-run it anywhere; the hashes match. In regulated and safety-critical settings, that is the difference between "trust us" and "check for yourself."
The same model verified bit-for-bit across three independent implementations — a GPU server, a laptop, and a web browser.
The same architecture built on conventional floating point, put through the same test.
Train a large float32 model, then compress it afterwards. Quality degrades. The model wasn't designed for the constraints.
Train directly in the target precision from step one. The model learns to be accurate within its constraints.
Our technology isn't just about smaller models today. The same properties that enable extreme quantisation unlock entirely new deployment paradigms.
BitBrain models running on standard hardware today — browsers, microcontrollers, edge devices — with measured public-benchmark results on audio, vision, and language, and replay receipts built in.
Streaming models keep per-session state in kilobytes, so modest CPU servers — or the user's own device — replace GPU fleets for live workloads. Larger language models are in training on the same recipe, with the audit trail built in.
A multiply-free, noise-resilient architecture maps directly onto analog circuits: a whole-number weight is a wire, a sum is currents meeting at a node. Physics does the compute — already validated in circuit simulation, with simulated analog memory drifting under a fifth of a percent per year.
Whether you need a model for a specific problem, want to license an existing BitBrain model, or are exploring what efficient AI could do for your product — we'd like to hear from you. Live demonstrations of every model family are available on request.