Add to ORKGThis work presents a new category of branch predictors designed to be addendums to existing state of the art prediction mechanisms. We call these neural network inspired predictors Shallow Online Neural (SON) Predictors as they utilize easily quantizable shallow networks and exhibit online training as opposed to other related works. This predictor is apt as both a branch prediction scheme and as a TAGE confidence predictor.M.S
Deep neural networks are state of the art methods for many learning tasks due to their ability to ex...
Abstract. Previous works have shown that neural branch prediction techniques achieve far lower mispr...
Previous works have shown that neural branch prediction techniques achieve far lower misprediction r...
In this thesis, BRAT is researched as a new hardware structure for cost-efficient branch prediction....
Abstract: The main aim of this short paper is to propose a new branch prediction approach called by ...
In this paper, we examine the application of simple neural processing elements to the problem of dyn...
This article presents a new and highly accurate method for branch prediction. The key idea is to use...
The state-of-the-art branch predictor, TAGE, remains inefficient at identifying correlated branches ...
Dynamic branch prediction in high-performance processors is a specific instance of a general time se...
Original article can be found at: http://www.sciencedirect.com/science/journal/13837621 Copyright El...
Abstract. This paper studies the architectural problem of branch prediction. We analyse the popular ...
Various models exist to predict a numerical value in supervised learning problems. One of the challe...
Branch predictors are very critical in modern superscalar processors and are responsible for achievi...
Abstract. The majority of currently available dynamic branch predictors base their prediction accura...
Page 1 Branch missprediction is a major bottleneck limiting processor performance. To improve branch...
Deep neural networks are state of the art methods for many learning tasks due to their ability to ex...
Abstract. Previous works have shown that neural branch prediction techniques achieve far lower mispr...
Previous works have shown that neural branch prediction techniques achieve far lower misprediction r...
In this thesis, BRAT is researched as a new hardware structure for cost-efficient branch prediction....
Abstract: The main aim of this short paper is to propose a new branch prediction approach called by ...
In this paper, we examine the application of simple neural processing elements to the problem of dyn...
This article presents a new and highly accurate method for branch prediction. The key idea is to use...
The state-of-the-art branch predictor, TAGE, remains inefficient at identifying correlated branches ...
Dynamic branch prediction in high-performance processors is a specific instance of a general time se...
Original article can be found at: http://www.sciencedirect.com/science/journal/13837621 Copyright El...
Abstract. This paper studies the architectural problem of branch prediction. We analyse the popular ...
Various models exist to predict a numerical value in supervised learning problems. One of the challe...
Branch predictors are very critical in modern superscalar processors and are responsible for achievi...
Abstract. The majority of currently available dynamic branch predictors base their prediction accura...
Page 1 Branch missprediction is a major bottleneck limiting processor performance. To improve branch...
Deep neural networks are state of the art methods for many learning tasks due to their ability to ex...
Abstract. Previous works have shown that neural branch prediction techniques achieve far lower mispr...
Previous works have shown that neural branch prediction techniques achieve far lower misprediction r...