Sparse approximate inverse preconditioners on high performance GPU platforms [PDF]
, 2016 Simulation with models based on partial differential equations often requires the solution of (sequences of) large and sparse algebraic linear systems. In multidimensional domains, preconditioned Krylov iterative solvers are often appropriate for these ...
Bertaccini, Daniele +1 more
core +1 more source
Near-Optimal Approximate Shortest Paths and Transshipment in Distributed and Streaming Models
, 2021 We present a method for solving the transshipment problem - also known as uncapacitated minimum cost flow - up to a multiplicative error of $1 + \varepsilon$ in undirected graphs with non-negative edge weights using a tailored gradient descent algorithm.
Becker, Ruben +3 more
core +1 more source
Low Power Neural Network by Reducing SRAM Operating Voltage
IEEE Access, 2022 With advancements in machine learning technology, networks are becoming increasingly complex, and the extent of the computation involved is increasing. Consequently, the computation time and power consumption of the learning process are increased.
Keisuke Kozu +3 more
doaj +1 more source
Circuit Aware Approximate System Design With Case Studies in Image Processing and Neural Networks
IEEE Access, 2019 This paper aims to exploit approximate computing units in image processing systems and artificial neural networks. For this purpose, a general design methodology is introduced, and approximation-oriented architectures are developed for different ...
Tuba Ayhan, Mustafa Altun
doaj +1 more source
Theory and experimental verification of configurable computing with stochastic memristors
Scientific Reports, 2021 The inevitable variability within electronic devices causes strict constraints on operation, reliability and scalability of the circuit design. However, when a compromise arises among the different performance metrics, area, time and energy, variability ...
Rawan Naous +9 more
doaj +1 more source
MACISH: Designing Approximate MAC Accelerators With Internal-Self-Healing
IEEE Access, 2019 Approximate computing studies the quality-efficiency trade-off to attain a best-efficiency (e.g., area, latency, and power) design for a given quality constraint and vice versa.
G. A. Gillani +5 more
doaj +1 more source
Optimized Inexact adder for Approximate Computing Applications [PDF]
JES: Journal of Engineering SciencesFor appropriate multimedia devices, power consumption should be less and it plays a major role in designing such devices. Image compression methods make use of a variety of signal processing architectures and algorithms.
NARMADHA G +4 more
doaj +1 more source
Computing equilibria of Cournot oligopoly models with mixed-integer quantities [PDF]
, 2017 We consider Cournot oligopoly models in which some variables represent indivisible quantities. These models can be addressed by computing equilibria of Nash equilibrium problems in which the players solve mixed-integer nonlinear problems.
Sagratella, Simone
core +1 more source
Accurate Sampling with Noisy Forces from Approximate Computing
Computation, 2020 In scientific computing, the acceleration of atomistic computer simulations by means of custom hardware is finding ever-growing application. A major limitation, however, is that the high efficiency in terms of performance and low power consumption ...
Varadarajan Rengaraj +3 more
doaj +1 more source
Machine Learning Using Approximate Computing
Journal of Low Power Electronics and ApplicationsApproximate computation has emerged as a promising alternative to accurate computation, particularly for applications that can tolerate some degree of error without significant degradation of the output quality.
Padmanabhan Balasubramanian +2 more
doaj +1 more source