154,362 research outputs found
Enlarging instruction streams
The stream fetch engine is a high-performance fetch architecture based on the concept of an instruction stream. We call a sequence of instructions from the target of a taken branch to the next taken branch, potentially containing multiple basic blocks, a stream. The long length of instruction streams makes it possible for the stream fetch engine to provide a high fetch bandwidth and to hide the branch predictor access latency, leading to performance results close to a trace cache at a lower implementation cost and complexity. Therefore, enlarging instruction streams is an excellent way to improve the stream fetch engine. In this paper, we present several hardware and software mechanisms focused on enlarging those streams that finalize at particular branch types. However, our results point out that focusing on particular branch types is not a good strategy due to Amdahl's law. Consequently, we propose the multiple-stream predictor, a novel mechanism that deals with all branch types by combining single streams into long virtual streams. This proposal tolerates the prediction table access latency without requiring the complexity caused by additional hardware mechanisms like prediction overriding. Moreover, it provides high-performance results which are comparable to state-of-the-art fetch architectures but with a simpler design that consumes less energy.Peer ReviewedPostprint (published version
Learning from accidents : machine learning for safety at railway stations
In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety
in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry
Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality
In the modern era, each Internet user leaves enormous amounts of auxiliary
digital residuals (footprints) by using a variety of on-line services. All this
data is already collected and stored for many years. In recent works, it was
demonstrated that it's possible to apply simple machine learning methods to
analyze collected digital footprints and to create psycho-demographic profiles
of individuals. However, while these works clearly demonstrated the
applicability of machine learning methods for such an analysis, created simple
prediction models still lacks accuracy necessary to be successfully applied for
practical needs. We have assumed that using advanced deep machine learning
methods may considerably increase the accuracy of predictions. We started with
simple machine learning methods to estimate basic prediction performance and
moved further by applying advanced methods based on shallow and deep neural
networks. Then we compared prediction power of studied models and made
conclusions about its performance. Finally, we made hypotheses how prediction
accuracy can be further improved. As result of this work, we provide full
source code used in the experiments for all interested researchers and
practitioners in corresponding GitHub repository. We believe that applying deep
machine learning for psycho-demographic profiling may have an enormous impact
on the society (for good or worse) and provides means for Artificial
Intelligence (AI) systems to better understand humans by creating their
psychological profiles. Thus AI agents may achieve the human-like ability to
participate in conversation (communication) flow by anticipating human
opponents' reactions, expectations, and behavior
iTrace: An Implicit Trust Inference Method for Trust-aware Collaborative Filtering
The growth of Internet commerce has stimulated the use of collaborative
filtering (CF) algorithms as recommender systems. A collaborative filtering
(CF) algorithm recommends items of interest to the target user by leveraging
the votes given by other similar users. In a standard CF framework, it is
assumed that the credibility of every voting user is exactly the same with
respect to the target user. This assumption is not satisfied and thus may lead
to misleading recommendations in many practical applications. A natural
countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take
account of the difference in the credibilities of the voting users when
performing CF. To this end, this paper presents a trust inference approach,
which can predict the implicit trust of the target user on every voting user
from a sparse explicit trust matrix. Then an improved CF algorithm termed
iTrace is proposed, which takes advantage of both the explicit and the
predicted implicit trust to provide recommendations with the CF framework. An
empirical evaluation on a public dataset demonstrates that the proposed
algorithm provides a significant improvement in recommendation quality in terms
of mean absolute error (MAE).Comment: 6 pages, 4 figures, 1 tabl
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
Machine-learned models are often described as "black boxes". In many
real-world applications however, models may have to sacrifice predictive power
in favour of human-interpretability. When this is the case, feature engineering
becomes a crucial task, which requires significant and time-consuming human
effort. Whilst some features are inherently static, representing properties
that cannot be influenced (e.g., the age of an individual), others capture
characteristics that could be adjusted (e.g., the daily amount of carbohydrates
taken). Nonetheless, once a model is learned from the data, each prediction it
makes on new instances is irreversible - assuming every instance to be a static
point located in the chosen feature space. There are many circumstances however
where it is important to understand (i) why a model outputs a certain
prediction on a given instance, (ii) which adjustable features of that instance
should be modified, and finally (iii) how to alter such a prediction when the
mutated instance is input back to the model. In this paper, we present a
technique that exploits the internals of a tree-based ensemble classifier to
offer recommendations for transforming true negative instances into positively
predicted ones. We demonstrate the validity of our approach using an online
advertising application. First, we design a Random Forest classifier that
effectively separates between two types of ads: low (negative) and high
(positive) quality ads (instances). Then, we introduce an algorithm that
provides recommendations that aim to transform a low quality ad (negative
instance) into a high quality one (positive instance). Finally, we evaluate our
approach on a subset of the active inventory of a large ad network, Yahoo
Gemini.Comment: 10 pages, KDD 201
EffiTest: Efficient Delay Test and Statistical Prediction for Configuring Post-silicon Tunable Buffers
At nanometer manufacturing technology nodes, process variations significantly
affect circuit performance. To combat them, post- silicon clock tuning buffers
can be deployed to balance timing bud- gets of critical paths for each
individual chip after manufacturing. The challenge of this method is that path
delays should be mea- sured for each chip to configure the tuning buffers
properly. Current methods for this delay measurement rely on path-wise
frequency stepping. This strategy, however, requires too much time from ex-
pensive testers. In this paper, we propose an efficient delay test framework
(EffiTest) to solve the post-silicon testing problem by aligning path delays
using the already-existing tuning buffers in the circuit. In addition, we only
test representative paths and the delays of other paths are estimated by
statistical delay prediction. Exper- imental results demonstrate that the
proposed method can reduce the number of frequency stepping iterations by more
than 94% with only a slight yield loss.Comment: ACM/IEEE Design Automation Conference (DAC), June 201
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