Vojtech Mrazek

TL;DR: The EvoApprox8b library provides Verilog, Matlab and C models of all approximate circuits and the error is given for seven different error metrics.

TL;DR: The paper showed the capability of the back propagation learning algorithm to adapt with NNs containing the approximate multipliers, and a methodology for the design of well-optimized power-efficient NNs with a uniform structure suitable for hardware implementation.

TL;DR: This article replaces the exact multipliers in two representative NNs with approximate designs to evaluate their effect on the classification accuracy and shows that using AMs can also improve the NN accuracy by introducing noise.

TL;DR: It is demonstrated that efficient approximations can be introduced into the computational path of DNN accelerators while retraining can completely be avoided, and a simple weight updating scheme is proposed that compensates the inaccuracy introduced by employing approximate multipliers.

TL;DR: In this paper, the authors propose a methodology for searching, selecting and combining the most suitable approximate circuits from a set of available libraries to generate an approximate accelerator for a given application, using machine learning techniques to create computational models estimating the overall quality of processing and hardware cost.