Resistive crossbars as approximate hardware building blocks for machine learning: Opportunities and challenges
Traditional computing systems based on the von Neumann architecture are fundamentally
bottlenecked by data transfers between processors and memory. The emergence of data …
bottlenecked by data transfers between processors and memory. The emergence of data …
Multiply accumulate operations in memristor crossbar arrays for analog computing
J Chen, J Li, Y Li, X Miao - Journal of Semiconductors, 2021 - iopscience.iop.org
Memristors are now becoming a prominent candidate to serve as the building blocks of non-
von Neumann in-memory computing architectures. By mapping analog numerical matrices …
von Neumann in-memory computing architectures. By mapping analog numerical matrices …
A fully hardware-based memristive multilayer neural network
Memristive crossbar arrays promise substantial improvements in computing throughput and
power efficiency through in-memory analog computing. Previous machine learning …
power efficiency through in-memory analog computing. Previous machine learning …
High-throughput in-memory computing for binary deep neural networks with monolithically integrated RRAM and 90-nm CMOS
Deep neural network (DNN) hardware designs have been bottlenecked by conventional
memories, such as SRAM due to density, leakage, and parallel computing challenges …
memories, such as SRAM due to density, leakage, and parallel computing challenges …
8-b precision 8-Mb ReRAM compute-in-memory macro using direct-current-free time-domain readout scheme for AI edge devices
JM Hung, TH Wen, YH Huang… - IEEE Journal of Solid …, 2022 - ieeexplore.ieee.org
Compute-in-memory (nvCIM) macros based on non-volatile memory make it possible for
artificial intelligence (AI) edge devices to perform energy-efficient multiply-and-accumulate …
artificial intelligence (AI) edge devices to perform energy-efficient multiply-and-accumulate …
Proposal of analog in-memory computing with magnified tunnel magnetoresistance ratio and universal STT-MRAM cell
In-memory computing (IMC) is an effective solution for energy-efficient artificial intelligence
applications. Analog IMC amortizes the power consumption of multiple sensing amplifiers …
applications. Analog IMC amortizes the power consumption of multiple sensing amplifiers …
Recent advances in synaptic nonvolatile memory devices and compensating architectural and algorithmic methods toward fully integrated neuromorphic chips
Nonvolatile memory (NVM)‐based neuromorphic computing has been attracting
considerable attention from academia and the industry. Although it is not completely …
considerable attention from academia and the industry. Although it is not completely …
Reconfigurable bit-serial operation using toggle SOT-MRAM for high-performance computing in memory architecture
J Wang, Y Bai, H Wang, Z Hao, G Wang… - … on Circuits and …, 2022 - ieeexplore.ieee.org
Computing in memory (CIM) is a promising candidate for high throughput and energy-
efficient data-driven applications, which mitigates the well-known memory bottleneck in Von …
efficient data-driven applications, which mitigates the well-known memory bottleneck in Von …
Comprehending in-memory computing trends via proper benchmarking
NR Shanbhag, SK Roy - 2022 IEEE Custom Integrated Circuits …, 2022 - ieeexplore.ieee.org
Since its inception in 2014 [1], the modern version of in-memory computing (IMC) has
become an active area of research in integrated circuit design globally for realizing artificial …
become an active area of research in integrated circuit design globally for realizing artificial …
A 40-nm, 64-kb, 56.67 TOPS/W voltage-sensing computing-in-memory/digital RRAM macro supporting iterative write with verification and online read-disturb detection
Computing-in-memory (CIM) architectures have gained importance in achieving high-
throughput energy-efficient artificial intelligence (AI) systems. Resistive RAM (RRAM) is a …
throughput energy-efficient artificial intelligence (AI) systems. Resistive RAM (RRAM) is a …