Maxpool operator for RISC-V processor

In this paper, it will be discussed about the development of a toolchain for integrating a convolutional neural network into a car's cabin, with emphasis on the compiler. The aim of the project is enabling the possibility of introducing the presence of an AI into the cabin, that would mainly supervise the driver for the sake of lowering the rate of any unfortunate event that may happen on the road while driving, like for example noticing and alerting the driver of sleepiness, dizziness. It may also supervise any other passenger as well. The compiler being used is LLVM, a project aiming to create a modular compiler, dividing itself into 3 main sections: the frontend module, the optimizer and the backend module. The current setup will be using the ONNX-MLIR project as a frontend module due to the intentions that an ONNX trained convolutional model will be used. As a backend module, an extended RISC-V module will be developed, because the company where the project is being developed, aims to create it's own hardware accelerator. Thus, the following aspects can be read in this paper: the implementation details about extending the RISC-V backend module for one's needs, additional information about integrating a virtual hardware simulator into a development environment, namely the Comet RISC-V simulator.