HIGH LEVEL SYNTHESIS USING RANDOM FOREST ALGORITHM

By Anmol Salvi

INTRODUCTION:-High level Synthesis is a method by which one can realize RTL(Register transfer Level) by taking an algorithmic description as input. The control algorithms are typically written in high level programming languages such as C , OpenCL and SystemC among others. The adoption of HLS however at an industrial level is still at the evaluation stage. There are 2 major bottlenecks that arise from the use of HLS tools:-

1)DSE(Design Space Exploration)-A substantial effort is needed for setting the micro-architecture constraints.

2)The long runtime of such HLS tools.

 

IMPLEMENTATION:-Before discussing the implementation its is crucial to know how the random forest algorithm works. The Random Forest Algorithm put simply is a supervised learning algorithm that builds multiple decision trees and merges them together to get a more accurate and stable prediction. Moving on to the implementation , two stages exist , the first being the training stage , followed by the refinement stage . In the training stage , knob settings are sampled in order to get our training set. Post this once our model begins to predict HLS results for all knob settings , the predicted Pareto set values are used to train the model with this Pareto set’s knob settings , which results in updating the training examples , retraining and updating parameters till a highly efficient learning model is achieved.

 

ADVANTAGES:-a)HLS knobs which provide binary choices are common. Tree based RF models can very easily handle such binary decisions by introducing a node with 2 branches separating the two decisions.

                           b)The RF algorithm is essentially composed of multiple regression trees . When a training set is provided it is internally and randomly partitioned to train individual trees.The final decision is then made by the collective vote from these individual trees. These two steps combined allow the reduction of generalization errors and prediction variance.

 

CONCLUSION:-Thus from the above points one can conclude that the way to go forward with the adoption of HLS in industries is to ensure fast and accurate results , which can be provided by integrating a supervised machine learning algorithm such as Random Forest with our HLS tools.

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