Related papers: Variability-aware Memristive Crossbars -- A Tutori…
Analog error correction codes have been proposed for analog in-memory computing on resistive crossbars, which can accelerate vector-matrix multiplication for machine learning. Unlike traditional communication or storage channels, this…
Resistive random-access memory (RRAM) is gaining popularity due to its ability to offer computing within the memory and its non-volatile nature. The unique properties of RRAM, such as binary switching, multi-state switching, and device…
Security is a growing problem that needs hardware support. Memristors provide an alternative technology for hardware-supported security implementation. This paper presents a specific technique that utilizes the benefits of hybrid…
Crossbar architectures have long been seen as a promising foundation for in-memory computing, using memristor arrays for high-density, energy-efficient analog computation. However, this conventional architecture suffers from a fundamental…
Memristors enable the computation of matrix-vector multiplications (MVM) in memory and, therefore, show great potential in highly increasing the energy efficiency of deep neural network (DNN) inference accelerators. However, computations in…
Recent advances in deep neural network demand more than millions of parameters to handle and mandate the high-performance computing resources with improved efficiency. The cross-bar array architecture has been considered as one of the…
Memristors are promising devices for scalable and low power, in-memory computing to improve the energy efficiency of a rising computational demand. The crossbar array architecture with memristors is used for vector matrix multiplication…
Memristor is a promising building block for the next generation nonvolatile random access memory and bio-inspired computing systems. Organizing memristors into high density crossbar arrays, although challenging, is critical to meet the…
Conceptual memristors have recently gathered wider interest due to their diverse application in non-von Neumann computing, machine learning, neuromorphic computing, and chaotic circuits. We introduce a compact CMOS circuit that emulates…
Given the growing focus on memristive crossbar-based in-memory computing (IMC) architectures as a potential alternative to current energy-hungry machine learning hardware, the availability of a fast and accurate circuit-level simulation…
Brain inspired circuits can provide an alternative solution to implement computing architectures taking advantage of fault tolerance and generalisation ability of logic gates. In this brief, we advance over the memristive threshold circuit…
Brain-inspired computing and neuromorphic hardware are promising approaches that offer great potential to overcome limitations faced by current computing paradigms based on traditional von-Neumann architecture. In this regard, interest in…
In recent times, Resistive RAMs (ReRAMs) have gained significant prominence due to their unique feature of supporting both non-volatile storage and logic capabilities. ReRAM is also reported to provide extremely low power consumption…
Memristor-based computer architectures are becoming more attractive as a possible choice of hardware for the implementation of neural networks. However, at present, memristor technologies are susceptible to a variety of failure modes, a…
In this paper, we show that the dynamics of a wide variety of nonlinear systems such as engineering, physical, chemical, biological, and ecological systems, can be simulated or modeled by the dynamics of memristor circuits. It has the…
We present a comprehensive phenomenological model for the crossbar integrated metal-oxide continuous-state memristors. The model consists of static and dynamic equations, which are obtained by fitting a large amount of experimental data,…
Crossbar arrays of resistive memories (RRAM) hold the promise of enabling In-Memory Computing (IMC), but essential challenges due to the impact of device imperfection and device endurance have yet to be overcome. In this work, we…
The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., Social Networks, Knowledge Graphs) on traditional computing architectures (CPU, GPU, or TPU). But the exploration of…
Recent years have seen a rapid rise of artificial neural networks being employed in a number of cognitive tasks. The ever-increasing computing requirements of these structures have contributed to a desire for novel technologies and…
In this paper a novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based…