SAMO: Optimised Mapping of Convolutional Neural Networks to Streaming Architectures

Toolflows that map Convolutional Neural Network (CNN) models to Field Programmable Gate Arrays (FPGAs) have been an important tool in accelerating a range of applications across different deployment settings. However, the significance of the problem of finding an optimal mapping is often overlooked, with the expectation that the end user will tune their generated hardware to their desired platform. This is particularly prominent within Streaming Architectures toolflows, where there is a large design space to explore. There have been many Streaming Architectures proposed, however apart from fpgaConvNet, there is limited support for optimisation methods that explore both performance objectives and platform constraints. In this work, we establish a framework, SAMO: a Streaming Architecture Mapping Optimiser, which generalises the optimisation problem of mapping Streaming Architectures to FPGA platforms. We also implement both Brute Force and Simulated Annealing optimisation methods in order to generate valid, high performance designs for a range of target platforms and CNN models. We are able to observe a 4x increase in performance compared to example designs for the popular Streaming Architecture framework FINN.