A general guide to applying machine learning to computer architecture

The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data. The ability of these algorithms to detect complex patterns in data which are extremely difficult to achieve manually, helps to produce effective predictive models. Whilst computer architects have been accelerating the performance of machine learning algorithms with GPUs and custom hardware, there have been few implementations leveraging these algorithms to improve the computer system performance. The work that has been conducted, however, has produced considerably promising results. The purpose of this paper is to serve as a foundational base and guide to future computer architecture research seeking to make use of machine learning models for improving system efficiency. We describe a method that highlights when, why, and how to utilize machine learning models for improving system performance and provide a relevant example showcasing the effectiveness of applying machine learning in computer architecture. We describe a process of data generation every execution quantum and parameter engineering. This is followed by a survey of a set of popular machine learning models. We discuss their strengths and weaknesses and provide an evaluation of implementations for the purpose of creating a workload performance predictor for different core types in an x86 processor. The predictions can then be exploited by a scheduler for heterogeneous processors to improve the system throughput. The algorithms of focus are stochastic gradient descent based linear regression, decision trees, random forests, artificial neural networks, and k-nearest neighbors.This work has been supported by the European Research Council (ERC) Advanced Grant RoMoL (Grant Agreemnt 321253) and by the Spanish Ministry of Science and Innovation (contract TIN 2015-65316P).Peer ReviewedPostprint (published version

A Behavior Analytic Account of Stereotype Threat

Although behavior analysis has contributed substantially to the understanding and study of learning in humans, cultural influences are often either overlooked or not accounted for in how they impact individuals in their day-to-day lives. One example in which this has occurred is in accounting for stereotypes. The field of Social Psychology has contributed a significant body of research on stereotypes and discusses in detail the conditions under which individuals are likely to be impacted by stereotypes. One common finding, often referred to as stereotype threat (Steele & Aronson, 1995), refers to how stereotypes can negatively impact individual performances under certain testing conditions. While data on stereotype threat indicates a clear pattern of decreases in performance scores for individuals in the threatened group, studies on stereotype threat have not examined: 1) whether stereotype threat occurs when arbitrary, non-stereotyped tasks are presented, 2) trends in individual data, or 3) how each individual is impacted by threat, lift, and neutral statements across similar tests. In addition, although researchers have offered many assumptions why stereotype threat occurs, none have evaluated the function of language in stereotype threat (c.f., Relational Frame Theory; Hayes, Barnes-Holmes, & Roche, 2001). The current study aimed to examine whether stereotype threat and stereotype lift by group affiliation (i.e., gender) would occur on an arbitrary, computer-based memory test and if other test-taking behaviors were affected by performance differences across four studies. Results indicated overall patterns consistent with the research base. Typical stereotype threat and lift patterns emerged more frequently when longer scripts were provided to participants prior to testing

Defining and identifying the knowledge economy in Scotland: a regional perspective on a global phenomenon

The development and growth of a knowledge economy has become a key policy aim forgovernments in all advanced economies. This is based on recognition that technologicalchange, the swift growth of global communications, and the ease of mobility of capital across national borders has dramatically changed the patterns of international trade and investment. The economic fate of individual nations is now inseparably integrated into the ebb and flow of the global economy. When companies can quickly move capital to those geographical locations which offer the best return, a country's long term prosperity is now heavily dependent on its abilityto retain the essential factors of production that are least mobile. This has led to apremium being placed on the knowledge and skills embodied in a country's labourforce, as it has become a widely accepted view that a country which possesses a high level of knowledge and skills in its workforce will have a competitive advantage overothers with a lower domestic skill base. Knowledge and skills are thought to be thebasis for the development of a knowledge economy

Natural‐language processing applied to an ITS interface

The aim of this paper is to show that with a subset of a natural language, simple systems running on PCs can be developed that can nevertheless be an effective tool for interfacing purposes in the building of an Intelligent Tutoring System (ITS). After presenting the special characteristics of the Smalltalk/V language, which provides an appropriate environment for the development of an interface, the overall architecture of the interface module is discussed. We then show how sentences are parsed by the interface, and how interaction takes place with the user. The knowledge‐acquisition phase is subsequently described. Finally, some excerpts from a tutoring session concerned with elementary geometry are discussed, and some of the problems and limitations of the approach are illustrated

Learning technologies and the lifelong learner: Armament or disarmament?

Educators at all levels are underpressure to produce ‘lifelong learners’. Their task is to ‘arm’ the student with knowledge and skills that will enable them to be creative and enterprising scholars. One possible way of arming the lifelong learner is through the use of learning technologies. Learning technologies can offer armament by widening access and participation and offering flexible delivery. This paper will use the results of two evaluation studies to explore the argument that learning technologies have the capacity to both arm and disarm students. Results from an evaluation of an email discussion list will be presented to highlight how the way a learning technology is used may arm a learner by giving them information but disarm them by promoting a lack of confidence and a low valuation of discussion. Results from an evaluation of a Microcosm application will be presented to highlight how the way a learning technology is used may arm a learner by helping them to apply knowledge but disarm them by placing restrictions on their self‐directed learning. These results will be discussed in order to argue that the ‘disarmament’ of students through the use of learning technologies may place obstacles in the way of lifelong learning