Characterization and physical modeling of endurance in embedded non-volatile memory technology

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Embedded memories, mostly implemented with static random access memory (SRAM), dominate the area and power of integrated circuits. Gain-cell embedded DRAM (GC-eDRAM) is an alternative to SRAM due to its high density, low power consumption, and two-ported f ...

HfO2-based ferroelectrics have dramatically changed the application perspectives of polarization-switching materials for information processing and storage. Their CMOS compatibility and preservation of high reversible polarization down to a few nanometer t ...

In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that ...

Among the different types of dynamic random-access memories (DRAMs), gain-cell embedded DRAM (GC-eDRAM) is a compact, low-power, and CMOS-compatible alternative to conventional static random-access memory (SRAM). GC-eDRAM achieves high memory density, as i ...

Technology scaling has progressed to enable integrated circuits with extremely high density enabling systems of tremendous complexity with manageable power consumption. With the continuation of Moore's law for many years, electronic chips have been able to ...

Non-Volatile Memory offers the possibility of implementing high-performance, durable data structures. However, achieving performance comparable to well-designed data structures in non-persistent (transient) memory is difficult, primarily because of the cos ...

The revolution of information-technology owes to silicon-based complementary-metal-oxide (CMOS) technology. However, CMOS technology approaches its physical limitation hardening the further progress of memory devices as well as computing paradigm requiring ...

Energy efficiency remains a challenge for the design of non-volatile resistive memories (ReRAMs) arrays. This memory technology suffers from intrinsic variability in switching speed, programming voltages and resistance levels. The programming conditions of ...

The need for running complex Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs), in edge devices, which are highly constrained in terms of computing power and energy, makes it important to execute such applications efficiently. ...

In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices, Here, we present an overview of the application of in ...

A persistent transactional memory (PTM) library provides an easy-to-use interface to programmers for using byte-addressable non-volatile memory (NVM). Previously proposed PTMs have, so far, been blocking. We present OneFile, the first wait-free PTM with in ...

Resistive Random Access Memories (ReRAMs) have been researched intensively in the last past decades as a promising alternatives technology for the next-generation non-volatile memory (NVM) devices. ReRAMâ s excellent performance properties such as high sw ...

Characterization and physical modeling of endurance in embedded non-volatile memory technology

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