What is happening
The device stores ten hypervectors, one per digit, each 4,096 bits wide. They were built from 60,000 handwritten digits in the MNIST dataset: for every digit, the bits that most of its examples agree on.
Your drawing is turned into a hypervector the same way, and then compared against all ten. The comparison is a Hamming distance — how many of the 4,096 bits differ — and the nearest one wins.
On an ordinary processor that comparison is 128 separate 32-bit operations per digit. On Sparsr it is one instruction, because the register is 4,096 bits wide and counting the differing bits is a mode on the result path rather than a second step. Ten digits, ten comparisons, twenty instructions including the loads.
What this does not tell you
Nothing about speed. The device here is a software model running in your browser, so its wall-clock time is a property of your laptop and not of Sparsr. The instruction counts on the page are exact; there is deliberately no timing figure beside them, because a number that looked like a measurement would not be one.
It will misread some of your digits. The stored digits were trained on other people's handwriting, and this classifier is a deliberately simple one: it scores 76.96% on the 10,000-image MNIST test set. That is far above guessing and far below what a neural network does. It is here because every vector operation in it runs on the device, which is the thing worth showing.