They support simple Boolean operations as well as complicated arithmetic operations such as multiplication in a single instruction, multiple data (SIMD) scheme. DSPs have evolved to support a wide range of applications requiring significant amounts of Boolean operations that may not even necessarily fit on the available lookup tables (LUTs) on an FPGA. In addition to the vast computation capabilities, DSP blocks support dynamic runtime programmability, which allows a single DSP block to be used as a different computational block in each clock cycle. Vendor synthesis tools provide capabilities to utilize the available resources on FPGAs; however, existing tool flows such as high-level synthesis tools fail to fully exploit the existing capabilities, especially the dynamic programmability of DSPs. Bajaj et al. [10-14] explore how DSP blocks can be deployed to produce high-throughput computational kernels and how their dynamic programmability can be exploited to create efficient implementations of arithmetic expressions. However, their solution suffers from inefficient mapping when it comes to implementing combinational Boolean functions using DSP blocks.

This tutorial describes how to use ESP32 Machine Learning. In more detail, it covers how to use an ESP32 KNN classifier to classify objects using their colors. To implement this ESP32 Machine Learning example, we will use a color sensor (TCS3200). This project derives from the Arduino Blog where it was used a KNN classifier to recognize different fruits. In this simple ESP32 KNN Machine Learning tutorial, we will replace the Arduino Nano 33 BLE with the ESP32 and we will add a color sensor because the ESP32 doesn't have a built-in sensor.