Notification Oriented Paradigm to Digital Hardware — A benchmark evaluation with Random Forest algorithm

The Notification Oriented Paradigm (NOP) emerges as an alternative to develop and execute applications. The NOP brings a new inference concept based on precise notifying collaborative minimal entities. This inference implicitly allows achieving decoupled solutions, thereby enabling parallelism at a granularity level as fine-grained as possible in the envisaged computational platform. Previous research has proposed a digital circuit solution based on the NOP model, which is called NOP to Digital Hardware (DH), as a sort of High-Level Synthesis (HLS) prototype tool. The results with NOP-DH were encouraging indeed. However, the previous NOP-DH works lack benchmarks that exploit well-known algorithms against known HLS tools, such as the Vivado HLS tool, which is one of the suitable commercial HLS solutions. This work proposes evaluating the NOP-DH applied to develop the well-known Random Forest algorithm. The Random Forest is a popular Machine Learning algorithm used in several classification and regression applications. Due to the high number of logic-causal evaluations in the Random Forest algorithm and the possibility of running them in parallel, it is suitable for envisaged benchmark purpose. Experiments were performed to compare NOP-DH, and two Vivado HLS approaches (an ad hoc code and a hls4ml tool-based code) in terms of performance, amount of logic elements, maximum frequency, and the number of predictions per second. Those experiments demonstrated that NOP-DH circuits achieve better results concerning the number of logical elements and prediction rates, with some scalability limitations as a drawback. On average, the NOP-DH uses 52.5% fewer resources, and the number of predictions per second is 4.7 times higher than Vivado HLS. Finally, our codes are made publicly available at https://nop.dainf.ct.utfpr.edu.br/nop-public/nop-dh-random-forest-algorithm.