Machine learning used to be executed in the cloud, then the inference part moved to the edge, and we’ve even seen micro-controllers able to do image recognition with GAP8 RISC-V micro-controller. But I’ve recently come across a white paper entitled “Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things” that shows how it’s possible to perform such tasks with very little resources. Here’s the abstract: This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices – such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2 KB RAM and 32 KB read-only flash. Bonsai maintains prediction accuracy while minimizing model size and prediction costs by: (a) developing a tree model which learns a single, shallow, sparse tree with powerful nodes; (b) sparsely projecting all data into […]
Bonsai Algorithm Enables Machine Learning on Arduino with a 2KB RAM Footprint
