Applicability of approximate multipliers in hardware neural networks

Stabilization of Nonlinear Control Systems through Using Zobov’s Theorem and Neural Networks

Zobov’s Theorem is one of the theorems which indicate the conditions for the stability of a nonlinear system with specific attraction region. We have applied neural networks to approximate some functions mentioned in Zobov’s theorem in order to find the controller of a nonlinear controlled system whose law in a mathematical manner is difficult to make. Finally, the effectiveness and the applica...

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks

Large-scale deep neural networks are both memoryintensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated in both industry and academia. Specific forms of binary neural networks (BNNs) and stochastic computingbased neural networks (SCNNs) are particularly appealing to h...

Implementation of High Performace Multipliers Based on Approximate Compressor Design

Estimating arithmetic is a design paradigm for DSP hardware. By allowing structurally incomplete arithmetic circuits to occasionally perform imprecise calculations, higher performance can be achieved in many different electronic systems. This paper presents a potential useful approach to implement tree multipliers by using estimating arithmetic. Experimental results show the applicability and e...

Solving nonlinear Lane-Emden type equations with unsupervised combined artificial neural networks

In this paper we propose a method for solving some well-known classes of Lane-Emden type equations which are nonlinear ordinary differential equations on the semi-innite domain. The proposed approach is based on an Unsupervised Combined Articial Neural Networks (UCANN) method. Firstly, The trial solutions of the differential equations are written in the form of feed-forward neural networks cont...