Results 31 to 40 of about 641,208 (284)
Templatized Fused Vector Floating-Point Dot Product for High-Level Synthesis
Journal of Low Power Electronics and Applications, 2022 Machine-learning accelerators rely on floating-point matrix and vector multiplication kernels. To reduce their cost, customized many-term fused architectures are preferred, which improve the latency, power, and area of the designs.
Dionysios Filippas +2 more
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Unbiased Rounding for HUB Floating-point Addition [PDF]
, 2018 Copyright (c) 2018 IEEE doi:10.1109/TC.2018.2807429Half-Unit-Biased (HUB) is an emerging format based on shifting the represented numbers by half Unit in the Last Place.
Gonzalez-Navarro, Sonia +2 more
core +1 more source
Noise Reduction Using Modified Wiener Filter in Digital Hearing Aid for Speech Signal Enhancement
Journal of Intelligent Systems, 2019 Speech signals are usually affected by noises during the communication process. For suppressing the noise signal that is combined with the speech signal, a Wiener filter is adapted in digital hearing aids.
Kumar Madam Aravind +1 more
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Radix Conversion for IEEE754-2008 Mixed Radix Floating-Point Arithmetic [PDF]
, 2013 Conversion between binary and decimal floating-point representations is ubiquitous. Floating-point radix conversion means converting both the exponent and the mantissa.
Kupriianova, O. +2 more
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Floating-Point LLL Revisited [PDF]
, 2005 Everybody knows the Lenstra-Lenstra-Lovász lattice basis reduction algorithm (LLL), which has proved invaluable in public-key cryptanalysis and in many other fields. Given an integer $d$-dimensional lattice basis which vectors have norms smaller than $B$, LLL outputs a so-called LLL-reduced basis in time $O(d^6 log^3 B)$, using arithmetic operations on
Nguyen, Phong Q., Stehlé, Damien
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AxCEM: Designing Approximate Comparator-Enabled Multipliers
Journal of Low Power Electronics and Applications, 2020 Floating-point multipliers have been the key component of nearly all forms of modern computing systems. Most data-intensive applications, such as deep neural networks (DNNs), expend the majority of their resources and energy budget for floating-point ...
Samar Ghabraei +3 more
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Parallel Algorithms for Summing Floating-Point Numbers
, 2016 The problem of exactly summing n floating-point numbers is a fundamental problem that has many applications in large-scale simulations and computational geometry.
Eldawy, Ahmed, Goodrich, Michael T.
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Design and Implementation of Numerical Linear Algebra Algorithms on Fixed Point DSPs
EURASIP Journal on Advances in Signal Processing, 2007 Numerical linear algebra algorithms use the inherent elegance of matrix formulations and are usually implemented using C/C++ floating point representation.
Nguyen Ha Thai +2 more
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MATEC Web of Conferences, 2016 This paper describes a single precision floating point division based on Newton-Raphson computational division algorithm. The Newton-Raphson computational algorithm is implemented using 32-bit floating point multi-plier and subtractor.
Singh Naginder, Sasamal Trailokya Nath
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Mixed-precision weights network for field-programmable gate array.
PLoS ONE, 2021 In this study, we introduced a mixed-precision weights network (MPWN), which is a quantization neural network that jointly utilizes three different weight spaces: binary {-1,1}, ternary {-1,0,1}, and 32-bit floating-point.
Ninnart Fuengfusin, Hakaru Tamukoh
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