11,908 research outputs found
Design of multimedia processor based on metric computation
Media-processing applications, such as signal processing, 2D and 3D graphics
rendering, and image compression, are the dominant workloads in many embedded
systems today. The real-time constraints of those media applications have
taxing demands on today's processor performances with low cost, low power and
reduced design delay. To satisfy those challenges, a fast and efficient
strategy consists in upgrading a low cost general purpose processor core. This
approach is based on the personalization of a general RISC processor core
according the target multimedia application requirements. Thus, if the extra
cost is justified, the general purpose processor GPP core can be enforced with
instruction level coprocessors, coarse grain dedicated hardware, ad hoc
memories or new GPP cores. In this way the final design solution is tailored to
the application requirements. The proposed approach is based on three main
steps: the first one is the analysis of the targeted application using
efficient metrics. The second step is the selection of the appropriate
architecture template according to the first step results and recommendations.
The third step is the architecture generation. This approach is experimented
using various image and video algorithms showing its feasibility
BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures
We introduce BriskStream, an in-memory data stream processing system (DSPSs)
specifically designed for modern shared-memory multicore architectures.
BriskStream's key contribution is an execution plan optimization paradigm,
namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair
of producer-consumer operators into consideration. We propose a branch and
bound based approach with three heuristics to resolve the resulting nontrivial
optimization problem. The experimental evaluations demonstrate that BriskStream
yields much higher throughput and better scalability than existing DSPSs on
multi-core architectures when processing different types of workloads.Comment: To appear in SIGMOD'1
Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things
The number of connected sensors and devices is expected to increase to billions in the near
future. However, centralised cloud-computing data centres present various challenges to meet the
requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput
and bandwidth constraints. Edge computing is becoming the standard computing paradigm for
latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related
to centralised cloud-computing models. Such a paradigm relies on bringing computation close to
the source of data, which presents serious operational challenges for large-scale cloud-computing
providers. In this work, we present an architecture composed of low-cost Single-Board-Computer
clusters near to data sources, and centralised cloud-computing data centres. The proposed
cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT
workload requirements while keeping scalability. We include an extensive empirical analysis to
assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data
centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud
architectures, and evaluate them through extensive simulation. We finally show that acquisition costs
can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209
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