Spiking neural networks (SNNs) implemented in neuromorphic computing architectures promise a high degree of bio-plausibility and energy efficiency compared to the artificial network (ANN). Thus, SNN-based spiking associative memories are preferred for capacity, area, energy-efficient (NAMs). While most previously published works focused on ANN-based NAM, this work implements full CMOS circuit memristor crossbar-based NAM first time. Instead using any software-based memristive SPICE model or...

This paper proposes a complete CMOS realized neuromorphic system for pattern recognition having CMOS-based memristor emulators as synaptic circuits. The crossbar array of the is modeled by considering emulator circuit's area to determine spacing between interconnects. interconnect parasitics are obtained from ANSYS Q3D extractor. Parasitics extracted also validated using analytical model. A architecture with parasitic components and will provide an understanding behavior can be used...

In this paper, a crossbar structure with CMOS based memristor emulator is presented where the spacing between modeled as per circuit's area for real-time design. The interconnect dimension in corresponds to 180 nm technology, and parasitics of are extracted using ANSYS Q3D extractor. parasitic components used design RC circuit model analyze signal delay different states sizes Cadence Virtuoso platform. results architecture provide an insight into how gets affected by state varying load...

Memristor is a computational and area efficient substitute for resistive synapse in neural networks as it provides tunable non-volatile storage of synaptic weights. Full CMOS circuit realisation 6 × Bidirectional Associative Memory (BAM) network with memristor implemented this paper. To show the BAM network's ability to recall its pattern, we have taken training set three Tetris pattern pairs, corresponding weights are calculated using MATLAB. We integrated proposed emulator crossbar storing...

Computation on a large volume of data at high speed and low power requires energy-efficient computing architectures. Spiking neural network (SNN) with bio-inspired spike-timing-dependent plasticity learning (STDP) is promising solution for neuromorphic systems than conventional artificial (ANN). Previous works SNN STDP primarily uses memristive devices which are difficult to fabricate. Some reported makes use memristor macro models, software-based cannot give complete insight into circuit...

The scalability of the crossbar array is critical for performing complex computation tasks, as large-scale synaptic arrays are required to complete operations. To address drawbacks a 2D neuromorphic IC, such long routing paths and large die area, 3D IC technology considered, where stacking enhances system's scalability. Therefore, novel architecture proposed folded decrease area by approximately 50%, thereby enhancing architecture's density 50%. In architecture, CMOS-based memristors...

The enhanced performance of neuromorphic computing over conventional Von Neumann results in high accuracy, energy and area efficient operations. systems process the information form spikes. Neural coding schemes is critical aspect as it defines relationship between input sensory spike train. Inter-spike-interval (ISI) encoding shows advantages density efficiency rate encoding. This paper analytical modelling Inter Spike Interval decoding scheme. scheme uses a CMOS implemented sample hold...

This paper demonstrates the complete Pavlov associative memory at CMOS transistor level. Unlike many works focusing on digital implementation with area-intensive and power-hungry circuits, proposed design for is realised in analog domain. Apart from learning forgetting shown various literature, circuit also incorporates other biologically inspired functions of memory, such as stimuli interval, variable rates, generalisation differentiation. Besides, most papers employ training by forming a...

The aim of designing the energy-efficient high-density spiking neural network (SNN) is not possible without channelising efforts to design basic repeating blocks Neuromorphic Computing (NMC), i.e., neurons and synapses. investigation done replace synapses in last years has created memristors as most prominent solution for SNN. CMOS-memristive integrated circuits are widely adopted realise brain-inspired neuromorphic circuits. While challenges have been highlighted resolved various...

There is a perpetual need of evolution in data converters to cater the demand high speed and accurate acquisition processing. The trainable neural can be trained using supervised learning techniques produce precise conversions. Such are PVT immune real time on-chip training signal generators. A digital analog converter needs labeled signals as signal. This paper proposes CMOS-memristor hybrid generator circuit memristive variable slope ramp design. Proposed architecture robust against...