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This paper attempts to discuss a selective overview of some recent trends & concepts in the field of unconventional (non-Von Neumann) computing paradigms using emerging non-volatile nanoscale resistive memory technology (RRAM). Case studies for dedicated nanoscale hardware used in non-spike based machine learning (ML) driven architectures such as- (i) Extreme Learning Machines (ELMs) and (ii)...
Restricted Boltzmann Machines (RBMs) offer a key methodology to implement Deep Learning paradigms. This paper presents a novel approach for realizing a hybrid RRAM-CMOS RBM architecture. In our proposed hybrid RBM architecture, HfOx based (filamentary-type switching) RRAM devices are extensively used to implement: (i) Synapses (ii) Internal neuron-state storage and (iii) Stochastic neuron activation...
In this paper, an Artificial Autoassociative Neural Network (AANN) is implemented by Network on Chip (NoC) architecture to solve communication and performance problem. This proposed NoC based system can map four neurons in one PE and the whole system consists of PEs each of which connects with a router. This system is reconfigurable and extendable so that it can easily suit for different applications...
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