Control of Resistive Switching in Mott Memories Based on TiN/AM4Q8/TiN MIM Devices
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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 ...
Ferroelectric materials offer a broad range of application-relevant properties, including spontaneous polarization switchable by electric field. Archetypical representatives of this class of materials are perovskites, currently used in applications ranging ...
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. ReRAMâs excellent performance properties such as high sw ...
In this thesis, we revisit classic problems in shared-memory distributed computing through the lenses of (1) emerging hardware technologies and (2) changing requirements. Our contributions consist, on the one hand, in providing a better understanding of th ...
In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory (PCM), where informa ...
IEEE2020
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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 ...
2020
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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 ...
2019
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Resistive-switching random access memory (ReRAM) technologies are nowadays a good candidate to overcome the bottleneck of Von Neumann architectures, taking advantage of their logic-in-memory capability and the ability to mimic biological synapse behavior. ...
AMER CHEMICAL SOC2019
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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. ...
2019
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Novel Deep Neural Network (DNN) accelerators based on crossbar arrays of non-volatile memories (NVMs)-such as Phase-Change Memory or Resistive Memory-can implement multiply-accumulate operations in a highly parallelized fashion. In such systems, computatio ...
