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Author: Admin | 2025-04-28
Large-density storage of data and rapid switching speed [312,313,314,315]. By applying an appropriate bias voltage to the RRAM device between its top and bottom electrode, it is possible to modulate its conductance. This can happen because of its flexible resistance switching characteristics between HRS and LRS. RRAM has a functional resemblance with biological synapse and thus acts as an artificial synapse in neuromorphic computing, and this incredible computational paradigm may fundamentally revolutionize the conventional Von Neumann computer device and outperform the modern computer architecture in data-intense applications [316,317,318,319].Basics of neural networksA typical neural network consists of neurons and synapses which process the inputs from the previous layer and propagate it to successive layers after computing the weighted sum of inputs. Neural networks are employed to solve complex problems in the domain of deep learning, machine learning applications such as pattern and speech recognition. The hardware implementation of neural networks demands large storage memory for computing the vector matrix product, thus making computation more energy intense. In neural networks, the input and weights are processed and stored in the form of vectors; hence, the computation is also referred to as vector matrix multiplication [303]. Figure 26a depicts the biological neural network in which the biological neuron processes input information using dendrites and then transmits it to other neurons using synapses [320]. The artificial neural network equivalent of biological neural network depicted in Fig. 26b consists of several neuron layers interconnected via synapses. Figure 26c shows the crossbar array architecture implemented in hardware to obtain the vector matrix multiplication. The crossbar array stores conductance values of matrix in the memory cell, and these memory units mimic the functionality of the biological synapse. The application of suitable voltage pulse to the crossbar (rows and columns) enables selection of a particular voltage cell thus paving pathway for in-memory computation that is efficient in terms of area, power and latency [321].RRAM devices can be utilized as synapses in providing the connection function between the information storage cells and neurons in the analog circuit of ANNs [309, 322,323,324,325]. The pivotal role of a synapse in a human brain is to transmit the impulses from one neuron to another during the process of information delivery in order to establish dynamic interconnections between two bonding neurons. The main function of an axon along with its terminals is to transmit the information out to other neurons (i.e. outputs), whereas dendrites are accountable for receiving the information (i.e. inputs). Triggering of a neuron releases a signal (pulse) that travels down the axon and into the dendrites of adjacent neurons via the synapses. The connection strength among the neurons or the synaptic weight decides the amount of signal that can reach the adjacent neuron. The small gaps (20–40 nm) between the axon terminal of a neuron and the dendrite of the other next neuron are referred to as synapses. As the brain starts adapting novel information, the synaptic weight becomes either stronger (potentiation) or weaker (depression) over the time through a
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