Foundational Concepts

Hyperdimensional Computing

Brian Guy (A.A.S.), Aurora Guy, and Sarah Guy (B.S.A. in Neurology), Solutions Prodigy.

Start Here: The Short Version

This method turns each item into a very long code of simple values. Similar items get similar codes. The system compares a new code with stored class codes. The closest match gives the answer.

What it can do

• It learns a class from one example or a few examples. A teacher shows the system one good sample. The system saves that sample as the class code. Later samples match that code with no long training.
• It joins facts into one code and can pull the facts back out. One fact can name an item. One fact can name a place. The system joins both facts into one code for later use.
• It keeps working when part of the code has noise or damage. Noise changes a few parts of the code. The rest of the code still holds the pattern. The system can still find the closest match.

Where people can use it

• Small sensors can sort signals on the device. A sensor on a pump can watch sound and shaking. The sensor can label a normal pump and a weak pump on the spot.
• Teams can search for items with a similar pattern. A team stores one code for each item. A new item gets a new code. The system finds stored codes that sit close to the new code.
• Systems can give simple and robust labels in noisy places. Factories have dust, heat, and loud sound. Farms have wind, rain, and rough ground. Field gear takes drops and hits in daily use.

Problems it can help solve

• Deep learning often needs many examples and long training. A small team has only a few good examples. This method learns from the few examples on hand.
• Small chips have low power and little memory. A big model drains a small chip fast. This method uses simple values and simple math. A small chip can run the method all day.
• Noise and small faults can break brittle features in normal models. One bad reading can change a normal result. This method spreads each pattern across many parts. One bad part changes the full code only a little.

The rest of this brief gives the history, the method, the 2026 gap, and a first project plan.

Concrete Examples

This section gives concrete uses for the method.

Example 1: an index over document parameters and audit trails

A document repository stores parameters and audit events for each document. An encoder assigns one random hypervector to each parameter name and each value. The encoder binds each name to its value. The encoder bundles the bound pairs into one record vector per document. Documents carry different parameters in the same fixed form. A new parameter type needs only one new codebook entry.

The encoder binds each actor to each audit action. The encoder rotates each pair by its step number. The encoder bundles the rotated pairs into one history vector per document. A query binds the record vector with a parameter name. The cleanup step compares the result against the value codebook.

A reference test used 10,000-bit vectors and four parameter pairs. The author query returned the correct value at similarity 0.685. The status query returned the correct value at similarity 0.692. An unrelated symbol scored 0.499 against the same record. A three-step history query returned the correct actor at similarity 0.745. A capacity test placed 30 pairs in one vector. Field queries failed zero times in 12 trials.

The compass pattern

An investigator compares one suspect history vector against stored history vectors. High similarity ranks candidate documents for review. The investigator opens the exact audit entries in the repository table. The hypervector index gives speed. The repository table gives proof. The index never replaces the audit table.

Bundle retrieval gives a probability, not a fact. A bundle cannot list its own events back in order. Long audit trails exceed the capacity of one vector. The exact store remains the evidence in every design.

Example 2: similar-case retrieval in health care

A clinic bundles symptom vectors and lab-value vectors into one case vector. A new case queries the store for the nearest past cases. The record system returns the full charts for those cases. The vector gives the shortlist. The chart gives the facts.

Example 3: fraud-pattern matching in banking

A bank encodes each transaction sequence with rotation by position. Confirmed fraud cases form prototype vectors from a few examples. Each new sequence scores against every prototype. High scores route the account to human review. The ledger remains the record of truth.

Example 4: support-ticket matching

A support desk encodes each ticket from word n-gram vectors. Each solved ticket stores one vector with a link to its fix. A new ticket retrieves the nearest solved tickets. Different wording for the same fault still matches. Agents start from a proven fix, not a blank page.

Example 5: failure signatures on field devices

A field device bundles vibration and temperature windows into a state vector. Failure classes form prototypes from one or a few examples. The device compares each new window on the chip itself. A match raises a local alert with no network round trip.

Example 6: a memory index for a wearable assistant

A wearable device records sound, images, and sensor readings through the day. An encoder turns each short segment into one hypervector. The encoder binds each segment vector to time and place vectors. The device stores the vectors beside the original recordings. A wearer asks about a past moment in plain words. The query vector retrieves the nearest segment vectors. The system opens the original recordings at those points. The index searches a long record at low power on the device. The recordings remain the true memory. The index only finds the moment faster.

One rule governs every example in this section. Hypervectors act as an index and a compass. Exact systems keep the data and the proof.

The Project

Goal. The desired end state is one developer toolkit and one reference edge classifier in daily use. Builders encode signals into hypervectors and classify new signals against stored class prototypes. Training takes one pass over the examples.

Proof target. The project proves one claim on one sensor or signal benchmark. The classifier reaches usable accuracy with one to ten examples per class. Training runs much faster than a small neural network baseline, and accuracy falls slowly as noise grows.

Product at the end. The product is the HDC Toolkit, version 1, and one reference classifier build. The toolkit contains an encoder library, a prototype store, a similarity engine, and a small API. The reference build runs on low-power hardware and sorts one signal type on the device.