Fault tolerance in vlsi using deep neural network.

The hardware implementation of deep neural

networks (DNNs) has recently received tremendous attention:

many applications in fact require high-speed operations that

suit a hardware implementation. However, numerous elements

and complex interconnections are usually required, leading

to a large area occupation and copious power consumption.

Stochastic computing (SC) has shown promising results for

low-power area-efficient hardware implementations, even though

existing stochastic algorithms require long streams that cause

long latencies. In this paper, we propose an integer form of

stochastic computation and introduce some elementary circuits.

We then propose an efficient implementation of a DNN based on

integral SC. The proposed architecture has been implemented

on a Virtex7 field-programmable gate array, resulting in 45%

and 62% average reductions in area and latency compared

with the best reported architecture in the literature. We also

synthesize the circuits in a 65-nm CMOS technology, and we

show that the proposed integral stochastic architecture results

in up to 21% reduction in energy consumption compared with

the binary radix implementation at the same misclassification

rate. Due to fault-tolerant nature of stochastic architectures,

we also consider a quasi-synchronous implementation that yields

33% reduction in energy consumption with respect to the binary

radix implementation without any compromise on performance.

the implementation of biologically inspired

artificial neural networks such as the restricted

Boltzmann machine (RBM) has aroused great interest due to

their high performance in approximating complicated func-

tions. A variety of applications can benefit from them, in

particular machine learning algorithms. They can be split into

two phases, which are referred to as learning and inference

phases.

Deep neural network (DNN)

Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.

Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

Reference

https://ieeexplore.ieee.org/document/7839313

https://ieeexplore.ieee.org/document/9196335