Branch predictor (BP) is an essential component in modern processors since high BP
accuracy can improve performance and reduce energy by decreasing the number of …

This paper presents a new method for branch prediction. The key idea is to use one of the
simplest possible neural networks, the perceptron, as an alternative to the commonly used …

This article presents a new and highly accurate method for branch prediction. The key idea
is to use one of the simplest possible neural methods, the perceptron, as an alternative to …

The TAGE predictor is often considered as state-of-the-art in conditional branch predictors
proposed by academy. In this paper, we first present directions to reduce the hardware …

Microarchitectural prediction based on neural learning has received increasing attention in
recent years. However, neural prediction remains impractical because its superior accuracy …

The state-of-the-art branch predictor, TAGE, remains inefficient at identifying correlated
branches deep in a noisy global branch history. We argue this inefficiency is a fundamental …

Modern data center applications have deep software stacks, with instruction footprints that
are orders of magnitude larger than typical instruction cache (I-cache) sizes. To efficiently …

Improved branch prediction accuracy is essential to sustaining instruction throughput with
today's deep pipelines. We introduce piecewise linear branch prediction, an idealized …

This paper explores the role of branch predictor organization in power/energy/performance
tradeoffs for processor design. We find that as a general rule, to reduce overall energy …

Modern processors employ a decoupled frontend with Fetch Directed Instruction Prefetching
(FDIP) to avoid frontend stalls in data center applications. However, the large branch …