Related papers: Computing with volatile memristors: An application…
This paper presents a novel design concept for spintronic nanoelectronics that emphasizes a seamless integration of spin-based memory and logic circuits. The building blocks are magneto-logic gates based on a hybrid graphene/ferromagnet…
Reservoir computing is a brain-inspired machine learning framework for processing temporal data by mapping inputs into high-dimensional spaces. Physical reservoir computers (PRCs) leverage native fading memory and nonlinearity in physical…
Spin-torque memristors were proposed in 2009, which could provide fast, low-power and infinite memristive behavior for large-density non-volatile memory and neuromorphic computing. However, the strict requirements of combining high…
The potential of memristive devices is often seeing in implementing neuromorphic architectures for achieving brain-like computation. However, the designing procedures do not allow for extended manipulation of the material, unlike CMOS…
The authors proposed and computationally analyzed nonvolatile static random access memory (NV-SRAM) architecture using metal-oxide-semiconductor field-effect transistor (MOSFET) type of spin-transistors referred to as pseudo-spin-MOSFET…
Memristors are an electronic device whose resistance depends on the voltage history that has been applied to its two terminals. Despite its clear advantage as a computational element, a suitable transport model is lacking for the special…
As a potential revolutionary topic in future information processing, mechanical computing has gained tremendous attention for replacing or supplementing conventional electronics vulnerable to power outages, security attacks, and harsh…
Implementing on-chip non-volatile optical memories has long been an actively pursued goal, promising significant enhancements in the capability and energy efficiency of photonic integrated circuits. Here, a novel optical memory has been…
This project explores the use of non-volatile synapses in neuromorphic computing for pattern recognition tasks through a comprehensive simulation-based approach. The main approach is through spintronic synapses, which leverage the…
The recent design of a nanoscale device with a memristive characteristic has had a great impact in nonlinear circuit theory. Such a device, whose existence was predicted by Leon Chua in 1971, is governed by a charge-dependent…
The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications. %, due to the intensive data movements. The decade-old idea of leveraging in-memory processing…
As data-intensive applications increasingly strain conventional computing systems, processing-in-memory (PIM) has emerged as a promising paradigm to alleviate the memory wall by minimizing data transfer between memory and processing units.…
Aside from recent advances in artificial intelligence (AI) models, specialized AI hardware is crucial to address large volumes of unstructured and dynamic data. Hardware-based AI, built on conventional complementary metal-oxidesemiconductor…
The advent of memristive devices offers a promising avenue for efficient and scalable analog computing, particularly for linear algebra operations essential in various scientific and engineering applications. This paper investigates the…
In this paper, we review the different memristive threshold logic (MTL) circuits that are inspired from the synaptic action of flow of neurotransmitters in the biological brain. Brain like generalisation ability and area minimisation of…
Memristive in-memory computing (IMC) has emerged as a promising solution for addressing the bottleneck in the Von Neumann architecture. However, the couplingbetweenthecircuitandalgorithm in IMC makes computing reliability susceptible to…
In this paper we discuss the potential of emerging spintorque devices for computing applications. Recent proposals for spinbased computing schemes may be differentiated as all-spin vs. hybrid, programmable vs. fixed, and, Boolean vs.…
In this paper, we briefly review the concept of memory circuit elements, namely memristors, memcapacitors and meminductors, and then discuss some applications by focusing mainly on the first class. We present several examples, their…
Neuromorphic devices, with their distinct advantages in energy efficiency and parallel processing, are pivotal in advancing artificial intelligence applications. Among these devices, memristive transistors have attracted significant…
A number of memristive devices, mainly ReRAMs, have been reported to exhibit a unique non-zero crossing hysteresis attributed to the interplay of resistive and not yet fully understood `capacitive', and `inductive' effects. This work…