100,668 research outputs found
Ways of Applying Artificial Intelligence in Software Engineering
As Artificial Intelligence (AI) techniques have become more powerful and
easier to use they are increasingly deployed as key components of modern
software systems. While this enables new functionality and often allows better
adaptation to user needs it also creates additional problems for software
engineers and exposes companies to new risks. Some work has been done to better
understand the interaction between Software Engineering and AI but we lack
methods to classify ways of applying AI in software systems and to analyse and
understand the risks this poses. Only by doing so can we devise tools and
solutions to help mitigate them. This paper presents the AI in SE Application
Levels (AI-SEAL) taxonomy that categorises applications according to their
point of AI application, the type of AI technology used and the automation
level allowed. We show the usefulness of this taxonomy by classifying 15 papers
from previous editions of the RAISE workshop. Results show that the taxonomy
allows classification of distinct AI applications and provides insights
concerning the risks associated with them. We argue that this will be important
for companies in deciding how to apply AI in their software applications and to
create strategies for its use
Automation from lean perspective-potentials and challenges
The competitive climate of production and high labour cost, motivate western companies to use technologies like automation as a mean to increase manufacturing competitiveness. On the other hand companies are aware about cost reductive policies like lean production which has shown noticeable achievement, consequently some manufacturers tend to follow such system. In this situation, in order to have lean enterprise, it is vital to find a clear picture of challenges and potentials of implementing automation within a lean environment. So, finding the right level and type of automation becomes vital for companies, and achieving this is not possible without a lean development of automation. The paper presents an overview of automation development from a lean perspective. The focus is on manufacturing and a case study in the automotive industry is presented. Challenges and potentials of automation are pinpointed and some suggestions regarding automation development is given
Energy Saving Techniques for Phase Change Memory (PCM)
In recent years, the energy consumption of computing systems has increased
and a large fraction of this energy is consumed in main memory. Towards this,
researchers have proposed use of non-volatile memory, such as phase change
memory (PCM), which has low read latency and power; and nearly zero leakage
power. However, the write latency and power of PCM are very high and this,
along with limited write endurance of PCM present significant challenges in
enabling wide-spread adoption of PCM. To address this, several
architecture-level techniques have been proposed. In this report, we review
several techniques to manage power consumption of PCM. We also classify these
techniques based on their characteristics to provide insights into them. The
aim of this work is encourage researchers to propose even better techniques for
improving energy efficiency of PCM based main memory.Comment: Survey, phase change RAM (PCRAM
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