Forms: Fine-grained polarized reram-based in-situ computation for mixed-signal dnn accelerator
Recent work demonstrated the promise of using resistive random access memory (ReRAM)
as an emerging technology to perform inherently parallel analog domain in-situ matrix …
as an emerging technology to perform inherently parallel analog domain in-situ matrix …
Spinel ferrites for resistive random access memory applications
Cutting edge science and technology needs high quality data storage devices for their
applications in artificial intelligence and digital industries. Resistive random access memory …
applications in artificial intelligence and digital industries. Resistive random access memory …
Towards ADC-less compute-in-memory accelerators for energy efficient deep learning
Compute-in-Memory (CiM) hardware has shown great potential in accelerating Deep Neural
Networks (DNNs). However, most CiM accelerators for matrix vector multiplication rely on …
Networks (DNNs). However, most CiM accelerators for matrix vector multiplication rely on …
Hybrid RRAM/SRAM in-memory computing for robust DNN acceleration
RRAM-based in-memory computing (IMC) effectively accelerates deep neural networks
(DNNs) and other machine learning algorithms. On the other hand, in the presence of RRAM …
(DNNs) and other machine learning algorithms. On the other hand, in the presence of RRAM …
Correctnet: Robustness enhancement of analog in-memory computing for neural networks by error suppression and compensation
A Eldebiky, GL Zhang, G Böcherer, B Li… - … , Automation & Test …, 2023 - ieeexplore.ieee.org
The last decade has witnessed the breakthrough of deep neural networks (DNNs) in many
fields. With the increasing depth of DNNs, hundreds of millions of multiply-and-accumulate …
fields. With the increasing depth of DNNs, hundreds of millions of multiply-and-accumulate …
DS-CIM: A 40nm Asynchronous Dual-Spike Driven, MRAM Compute-In-Memory Macro for Spiking Neural Network
Compute-in-memory (CIM) based on emerging nonvolatile memory (eNVM) is an effective
way to deploy neural networks to low-power edge devices for both storage and computation …
way to deploy neural networks to low-power edge devices for both storage and computation …
Exploring model stability of deep neural networks for reliable RRAM-based in-memory acceleration
RRAM-based in-memory computing (IMC) effectively accelerates deep neural networks
(DNNs). Furthermore, model compression techniques, such as quantization and pruning …
(DNNs). Furthermore, model compression techniques, such as quantization and pruning …
Robust RRAM-based in-memory computing in light of model stability
Resistive random-access memory (RRAM)-based in-memory computing (IMC) architectures
offer an energy-efficient solution for DNN acceleration. However, the performance of RRAM …
offer an energy-efficient solution for DNN acceleration. However, the performance of RRAM …
XCRYPT: Accelerating Lattice-Based Cryptography With Memristor Crossbar Arrays
This article makes a case for accelerating lattice-based postquantum cryptography with
memristor-based crossbars. We map the polynomial multiplications in a representative …
memristor-based crossbars. We map the polynomial multiplications in a representative …
When in-memory computing meets spiking neural networks—A perspective on device-circuit-system-and-algorithm co-design
This review explores the intersection of bio-plausible artificial intelligence in the form of
spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain …
spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain …