Yazar "Kadayif, I." seçeneğine göre listele

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    Öğe
    Compiler-directed code restructuring for reducing data TLB energy
    (Association for Computing Machinery (ACM), 2004) Kandemir, M.; Kadayif, I.; Chen, G.
    Prior work on TLB power optimization considered circuit and architectural techniques. A recent software-based technique for data TLBs has considered the possibility of storing the frequently used virtual-to-physical address translations in a set of translation registers (TRs), and using them when necessary instead of going to the data TLB. This paper presents a compiler-based strategy for increasing the effectiveness of TRs. The idea is to restructure the application code in such a fashion that once a TR is loaded, its contents are reused as much as possible. Our experimental evaluation with six array-based benchmarks from the Spec2000 suite indicates that the proposed TR reuse strategy brings significant reductions in data TLB energy over an alternate strategy that employs TRs but does not restructure the code for TR reuse.
  • [ X ]
    Öğe
    Tuning in-sensor data filtering to reduce energy consumption in wireless sensor networks
    (2004) Kadayif, I.; Kandemir, M.
    In recent years, research on wireless sensor networks has been undergoing a revolution, promising to have significant impact on a broad range of applications from military to health care to food safety. An important problem in many sensor network applications is to decide the amount of computation (or filtering) that needs to be done in the sensor nodes before the data are shifted to a central base station. Right amount of data filtering in the sensor nodes can lead to large savings in network-wide energy consumption. The main goal of this paper is to develop an automated strategy for data filtering in wireless sensor nodes. Assuming that one needs to reduce the overall energy consumption (as opposed to reducing just computation energy or communication energy), the proposed strategy attempts to strike a balance between computation energy consumption and communication energy consumption. Our experimental results clearly indicate that the proposed data filtering strategy generates substantial energy savings in practice.