In-memory computing with resistive memory circuits: Status and outlook
G Pedretti, D Ielmini - Electronics, 2021 - mdpi.com
In-memory computing (IMC) refers to non-von Neumann architectures where data are
processed in situ within the memory by taking advantage of physical laws. Among the …
processed in situ within the memory by taking advantage of physical laws. Among the …
A survey of in-spin transfer torque MRAM computing
In traditional von Neumann computing architectures, the essential transfer of data between
the processor and memory hierarchies limits the computational efficiency of next-generation …
the processor and memory hierarchies limits the computational efficiency of next-generation …
Accelerating deep neural network in-situ training with non-volatile and volatile memory based hybrid precision synapses
Compute-in-memory (CIM) with emerging non-volatile memories (eNVMs) is time and
energy efficient for deep neural network (DNN) inference. However, challenges still remain …
energy efficient for deep neural network (DNN) inference. However, challenges still remain …
Heterogeneous mixed-signal monolithic 3-D in-memory computing using resistive RAM
Resistive random access memory (RRAM)-based compute-in-memory architecture helps
overcome the bottleneck caused by large memory transactions in the convolutional neural …
overcome the bottleneck caused by large memory transactions in the convolutional neural …
Sub-nA low-current HZO ferroelectric tunnel junction for high-performance and accurate deep learning acceleration
This paper presents a unique opportunity of HZO ferroelectric tunnel junction (FTJ) for in-
memory computing. The device operates at an extremely low sub-nA current while …
memory computing. The device operates at an extremely low sub-nA current while …
Computing-in-memory neural network accelerators for safety-critical systems: Can small device variations be disastrous?
Computing-in-Memory (CiM) architectures based on emerging nonvolatile memory (NVM)
devices have demonstrated great potential for deep neural network (DNN) acceleration …
devices have demonstrated great potential for deep neural network (DNN) acceleration …
In-memory computing for machine learning and deep learning
In-memory computing (IMC) aims at executing numerical operations via physical processes,
such as current summation and charge collection, thus accelerating common computing …
such as current summation and charge collection, thus accelerating common computing …
Device quantization policy in variation-aware in-memory computing design
Device quantization of in-memory computing (IMC) that considers the non-negligible
variation and finite dynamic range of practical memory technology is investigated, aiming for …
variation and finite dynamic range of practical memory technology is investigated, aiming for …
A variation robust inference engine based on STT-MRAM with parallel read-out
STT-MRAM is a promising candidate as embedded non-volatile memory (NVM) at 28nm and
beyond. Due to its limited on/off ratio, STT-MRAM is often used as digital memory that only …
beyond. Due to its limited on/off ratio, STT-MRAM is often used as digital memory that only …
Toward energy-efficient STT-MRAM design with multi-modes reconfiguration
CMOS compatible spin-transfer-torque magnetic random access memory (STT-MRAM) has
demonstrated promising developments as the next-generation embedded non-volatile …
demonstrated promising developments as the next-generation embedded non-volatile …