Better branch prediction through prophet/critic hybrids

The prophet/critic hybrid conditional branch predictor has two component predictors. The prophet uses a branch's history to predict its direction. We call this prediction and the ones for branches following it the branch future. The critic uses the branch's history and future to critique the prophet's prediction. The hybrid combines the prophet's prediction with the critique, either agrees or disagree, forming the branch's overall prediction. Results shows these hybrids can reduce mispredicts by 39 percent and improve processor performance by 7.8 percent.Peer ReviewedPostprint (published version

Branch Prediction as a Reinforcement Learning Problem: Why, How and Case Studies

Recent years have seen stagnating improvements to branch predictor (BP) efficacy and a dearth of fresh ideas in branch predictor design, calling for fresh thinking in this area. This paper argues that looking at BP from the viewpoint of Reinforcement Learning (RL) facilitates systematic reasoning about, and exploration of, BP designs. We describe how to apply the RL formulation to branch predictors, show that existing predictors can be succinctly expressed in this formulation, and study two RL-based variants of conventional BPs.Comment: 6 pages, appeared in ML workshop for Computer Architecture and Systems 202

Hybrid branch prediction for pipelined MIPS processor

In the modern microprocessors that designed with pipeline stages, the performance of these types of processors will be affected when executing branch instructions, because in this case there will be stalls in the pipeline. In turn this causes in reducing the Cycle Per Instruction (CPI) of the processor. In the case of executing a branch instruction, the processor needs an extra clocks to know if that branch will happen (Taken) or not (Not Taken) and also it requires calculating the new address in the case of the branch is Taken. The prediction that the branch is T / NT is an important stage in enhancing the processor performance. In this research more than one method of branch prediction (hybrid) is used and the designed circuit will choose different types of prediction algoritms depending on the type of the branch. Some of these methods were used are static while the other are dynamic. All circuits were built practically and examined by applying different programs on the designed predictor algorithm to compute the performance of the processor

Branch Prediction as a Reinforcement Learning Problem: Why, How and Case Studies

Recent years have seen stagnating improvements to branch predictor (BP) efficacy and a dearth of fresh ideas in branch predictor design, calling for fresh thinking in this area. This paper argues that looking at BP from the viewpoint of Reinforcement Learning (RL) facilitates systematic reasoning about, and exploration of, BP designs. We describe how to apply the RL formulation to branch predictors, show that existing predictors can be succinctly expressed in this formulation, and study two RL-based variants of conventional BPs

Mobile Edge Computing

This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists

The Ridicule of Time: Science Fiction, Bioethics, and the Posthuman

The article traces two phases of SF about human species change, the first in the 1940s and early 1950s, the so called “golden age" of SF. In this first phase the advent of the posthuman is brought on by eugenics or sudden mutations caused by fallout from nuclear war. It consists of well-known books by most of the leading authors of the period: Clarke's Childhood's End, Sturgeon's More Than Human, Van Vogt's Slan, Heinlein's Beyond This Horizon and Methuselah's Children, and a number of lesser known texts. The second phase got under way in the late-1970s and lasted up until just before the millennium. Stimulated by excitement over recombinant DNA and the first test-tube baby in 1978, the surge of interest in genetic transformations of the human explored genetic engineering rather than evolution as the source of the posthuman. The fiction considered includes Octavia Butler's The Xenogenesis Trilogy, Greg Bear's Darwin's Radio and its sequel, Bruce Sterling's Schismatrix. The article concludes with a look at a third group of books, nonfiction about the posthuman by bioethicists and policy experts published since 2002. I characterize this last body of work as either variants of the American jeremiad Sacvan Bercovitch described or as "anticipations" in the optative mode of popular science writing pioneered by H. G. Wells in his book by that name, Anticipations (1901). Throughout the article, I emphasize the covert relationship between nonfiction policy works—for and against genetic enhancement—to what Istvan Cscicsery-Ronay called "science-fictional habits of mind . . .a mode of response that frames and tests experiences as if they were aspects of a work of science fiction.

McDougal-Lasswell Policy Science: Death and Transfiguration

This article discusses the death and transfiguration of the legal paradigm referred to as McDougal-Lasswell Policy Science. This paradigm asserts those who make legal decisions should decide on articulated policy grounds rather than attempting to make decisions based merely on rules or principles. The theme centers on the paradox to which jurists have given different degrees of acceptance. In the United States domestic scene, it is virtually dead, and in the international law arena where it is transfigured, it is alive and well

A Survey of Learning-based Automated Program Repair

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance. With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories. Such learning-based techniques usually treat APR as a neural machine translation (NMT) task, where buggy code snippets (i.e., source language) are translated into fixed code snippets (i.e., target language) automatically. Benefiting from the powerful capability of DL to learn hidden relationships from previous bug-fixing datasets, learning-based APR techniques have achieved remarkable performance. In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the learning-based APR community. We illustrate the general workflow of learning-based APR techniques and detail the crucial components, including fault localization, patch generation, patch ranking, patch validation, and patch correctness phases. We then discuss the widely-adopted datasets and evaluation metrics and outline existing empirical studies. We discuss several critical aspects of learning-based APR techniques, such as repair domains, industrial deployment, and the open science issue. We highlight several practical guidelines on applying DL techniques for future APR studies, such as exploring explainable patch generation and utilizing code features. Overall, our paper can help researchers gain a comprehensive understanding about the achievements of the existing learning-based APR techniques and promote the practical application of these techniques. Our artifacts are publicly available at \url{https://github.com/QuanjunZhang/AwesomeLearningAPR}