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

Learning models for semantic classification of insufficient plantar pressure images

Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields

Remembering today tomorrow: exploring the human-centred design of digital mementos

This paper describes two-part research exploring the context for and human-centred design of ‘digital mementos’, as an example of technology for reflection on personal experience(in this case, autobiographical memories). Field studies into families’ use of physical and digital objects for remembering provided a rich understanding of associated user needs and human values, and suggested properties for ‘digital mementos’ such as being ‘not like work’, discoverable and fun. In a subsequent design study, artefacts were devised to express these features and develop the understanding of needs and values further via discussion with groups of potential ‘users’. ‘Critical artefacts’(the products of Critical Design)were used to enable participants to envisage broader possibilities for social practices and applications of technology in the context of personal remembering, and thus to engage in the design of novel devices and systems relevant to their lives. Reflection was a common theme in the work, being what the digital mementos were designed to afford and the mechanism by which the design activity progressed. Ideas for digital mementos formed the output of this research and expressed the designer’s and researcher’s understanding of participants’ practices and needs, and the human values that underlie them and, in doing so, suggest devices and systems that go beyond usability to support a broader conception of human activity