Related papers: Nanocryotron-driven Charge Configuration Memristor
In-memory computing (IMC) is an emerging non-von Neumann paradigm that leverages the intrinsic physics of memory devices to perform computations directly within the memory array. Among the various candidates, phase-change memory (PCM) has…
CMOS technology and its continuous scaling have made electronics and computers accessible and affordable for almost everyone on the globe; in addition, they have enabled the solutions of a wide range of societal problems and applications.…
We demonstrate a non-volatile magnetoelectric magnonic memory (MEMM) that enables fully electrical write/read via direct magnon-driven sensing in an insulating antiferromagnet. A fabricated SrIrO3/La-BiFeO3/SrIrO3 trilayer exhibits sub-100…
Large-capacity Content Addressable Memory (CAM) is a key element in a wide variety of applications. The inevitable complexities of scaling MOS transistors introduce a major challenge in the realization of such systems. Convergence of…
Magnetic skyrmions are promising candidates for logic-in-memory applications, intrinsically merging high density non-volatile data storage with computing capabilities, owing to their nanoscale size, fast motion, and mutual repulsions.…
The surging interest in quantum computing, space electronics, and superconducting circuits has led to new developments in cryogenic data storage technology. Quantum computers promise to far extend our processing capabilities and may allow…
We report the design-technology co-optimization (DTCO) scheme to develop a 28-nm cryogenic CMOS (Cryo-CMOS) technology for high-performance computing (HPC). The precise adjustment of halo implants manages to compensate the threshold voltage…
The advent of nanoscale memristors raised hopes of being able to build CMOL (CMOS/nanowire/moLecular) type ultra-dense in-memory-computing circuit architectures. In CMOL, nanoscale memristors would be fabricated at the intersection of…
Memristive devices are promising elements for energy-efficient neuromorphic computing and future artificial intelligence systems. For diffusive memristors, the device state switching occurs because of the sequential formation and…
An atomistic effective Hamiltonian technique is used to investigate the finite-temperature energy storage properties of a ferroelectric nanocomposite consisting of an array of BaTiO$_{3}$ nanowires embedded in a SrTiO$_{3}$ matrix, for…
Neuromorphic computing promises to transform the current paradigm of traditional computing towards Non-Von Neumann dynamic energy-efficient problem solving. Thus, dynamic memory devices capable of simultaneously performing nonlinear…
Due to stringent thermal budgets in cryogenic technologies such as superconducting quantum computers and sensors, minimizing the energy dissipation and power consumption of cryogenic electronic components is pivotal for large-scale devices.…
Superconductor electronics (SCE) is a promising complementary and beyond CMOS technology. However, despite its practical benefits, the realization of SCE logic faces a significant challenge due to the absence of dense and scalable…
Neurons with internal memory have been proposed for biological and bio-inspired neural networks, adding interesting functionality. We propose and model a nanoscale optoelectronic neural node with charge-based time-limited memory and signal…
Transformer decoding is constrained by both attention compute and KV-cache movement. This paper presents the Ferroelectric Charge-Domain Compute Cell (FCDC), a hafnium-zirconium-oxide (HZO) memcapacitor with an access device that stores…
This paper presents a memristor-based compute-in-memory hardware accelerator for on-chip training and inference, focusing on its accuracy and efficiency against device variations, conductance errors, and input noise. Utilizing realistic…
Ferroelectric-based capacitive crossbar arrays have been proposed for energy-efficient in-memory computing in the charge domain. They combat the challenges like sneak paths and high static power faced by resistive crossbar arrays but are…
Developing ultra-low-energy superconducting computing and fault-tolerant quantum computing will require scalable superconducting memory. While conventional superconducting logic-based memory cells have facilitated early demonstrations,…
The advent of deep learning has resulted in a number of applications which have transformed the landscape of the research area in which it has been applied. However, with an increase in popularity, the complexity of classical deep neural…
In conventional digital computers, data and information are represented in binary form and encoded in the steady states of transistors. They are then processed in a quasi-static way. However, with transistors approaching their physical…