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it - Information Technology 64(3) - Juni 2022

https://dl.gi.de/handle/20.500.12116/39743
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Auflistung it - Information Technology 64(3) - Juni 2022 nach Schlagwort "Approximate Computing"

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  • Zeitschriftenartikel
    Approximate Computing
    (it - Information Technology: Vol. 64, No. 3, 2022) Keszocze, Oliver
  • Zeitschriftenartikel
    Design and error analysis of accuracy-configurable sequential multipliers via segmented carry chains
    (it - Information Technology: Vol. 64, No. 3, 2022) Echavarria, Jorge; Wildermann, Stefan; Keszocze, Oliver; Khosravi, Faramarz; Becher, Andreas; Teich, Jürgen
    We present the design and a closed-form error analysis of accuracy-configurable multipliers via segmented carry chains. To address this problem, we model the approximate partial-product accumulations as a sequential process. According to a given splitting point of the carry chains, the technique herein discussed allows varying the quality of the accumulations and, consequently, the overall product. Due to these shorter critical paths, such kinds of approximate multipliers can trade-off accuracy for an increased performance whilst exploiting the inherent area savings of sequential over combinatorial approaches. We implemented multiple architectures targeting FPGAs and ASICs with different bit-widths and accuracy configurations to 1) estimate resources, power consumption, and delay, as well as to 2) evaluate those error metrics that belong to the so-called #P-complete class.
  • Zeitschriftenartikel
    Unlocking approximation for in-memory computing with Cartesian genetic programming and computer algebra for arithmetic circuits
    (it - Information Technology: Vol. 64, No. 3, 2022) Froehlich, Saman; Drechsler, Rolf
    With ReRAM being a non-volative memory technology, which features low power consumption, high scalability and allows for in-memory computing, it is a promising candidate for future computer architectures. Approximate computing is a design paradigm, which aims at reducing the complexity of hardware by trading off accuracy for area and/or delay. In this article, we introduce approximate computing techniques to in-memory computing. We extend existing compilation techniques for the Programmable Logic in-Memory (PLiM) computer architecture, by adapting state-of-the-art approximate computing techniques for arithmetic circuits. We use Cartesian Genetic Programming for the generation of approximate circuits and evaluate them using a Symbolic Computer Algebra-based technique with respect to error-metrics. In our experiments, we show that we can outperform state-of-the-art handcrafted approximate adder designs.