Fuzzy logic based energy and throughput aware design space exploration for MPSoCs

Multicore architectures were introduced to mitigate the issue of increase in power dissipation with clock frequency. Introduction of deeper pipelines, speculative threading etc. for single core systems were not able to bring much increase in performance as compared to their associated power overhead. However for multicore architectures performance scaling with number of cores has always been a challenge. The Amdahl's law shows that the theoretical maximum speedup of a multicore architecture is not even close to the multiple of number of cores. With less amount of code in parallel having more number of cores for an application might just contribute in greater power dissipation instead of bringing some performance advantage. Therefore there is a need of an adaptive multicore architecture that can be tailored for the application in use for higher energy efficiency. In this paper a fuzzy logic based design space exploration technique is presented that is targeted to optimize a multicore architecture according to the workload requirements in order to achieve optimum balance between throughput and energy of the system

Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

Neuroarquitectura: percepción de cambios de la atmósfera

[Neuroarchitecture: perception of changes in the atmosphere

Suunnittelutason parametrisointi Kactus2:ssa

Embedded systems are growing larger and more complex. Even now, current system designs can contain hundreds of Intellectual Property (IP) components. To keep up with productivity, the reusability of the IP components must be improved. This is the scope of IEEE standard IP-XACT. This thesis is based on Kactus2, an open source IP-XACT tool developed at Tampere University of Technology. Kactus2 provides a graphical user interface for System-on-Chip and embedded system IP packing, design capture and VHDL/Verilog code generation. This thesis describes the development and implementation of version 2.8 of Kactus2. The requirements and solutions are presented in detail for each of the new features and improvements implemented in version 2.8. Alternative solutions are presented, and the selected alternatives are justified. Possible future implementations are also given. In version 2.8, the parameter usage of IP components is improved through the use of universally unique IDs (UUID), which requires many changes e.g. to the IP-XACT Component editors. New features include parameter importing, design level configuration through parameters and a parameter propagation mechanism. Remapping IP-XACT Memory Maps through the use of Remap States and memory Remap Elements has also been added. To facilitate the storing of hierarchical IP components, a new save action has been added to the Kactus2 toolbar. Version 2.8 of Kactus2 was released according to its schedule. The development of the version is considered a success, as it improves the design level parameterization in Kactus2 while incorporating additional new features. Within a month of its release, Kactus2 version 2.8 has been downloaded over 200 times, and its benefits over the previous version are confirmed by industrial System-on-Chip developers

Explora : interactive querying of multidimensional data in the context of smart cities

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving-on ingestion time-synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach