Research progress on memristor: From synapses to computing systems
As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …
transistors has been reduced very near to the minimum physically-realizable channel length …
An overview of processing-in-memory circuits for artificial intelligence and machine learning
Artificial intelligence (AI) and machine learning (ML) are revolutionizing many fields of study,
such as visual recognition, natural language processing, autonomous vehicles, and …
such as visual recognition, natural language processing, autonomous vehicles, and …
A 40-nm MLC-RRAM compute-in-memory macro with sparsity control, on-chip write-verify, and temperature-independent ADC references
Resistive random access memory (RRAM)-based compute-in-memory (CIM) has shown
great potential for accelerating deep neural network (DNN) inference. However, device …
great potential for accelerating deep neural network (DNN) inference. However, device …
VSDCA: A voltage sensing differential column architecture based on 1T2R RRAM array for computing-in-memory accelerators
Non-volatile memory (NVM) such as RRAM and PCM has become the key component in
high energy efficiency computing-in-memory (CIM) architectures. However, the computing …
high energy efficiency computing-in-memory (CIM) architectures. However, the computing …
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 …
ENNA: An efficient neural network accelerator design based on ADC-free compute-in-memory subarrays
Compute-in-memory (CIM) is an attractive solution for machine learning hardware
acceleration since it merges computation directly into memory arrays, performing parallel …
acceleration since it merges computation directly into memory arrays, performing parallel …
Mac-ecc: In-situ error correction and its design methodology for reliable nvm-based compute-in-memory inference engine
Compute-in-memory (CIM) employing non-volatile memories (NVMs) has been widely
investigated as an attractive candidate to accelerate the heavy multiply-and-accumulate …
investigated as an attractive candidate to accelerate the heavy multiply-and-accumulate …
A 1T2R1C ReRAM CIM accelerator with energy-efficient voltage division and capacitive coupling for CNN acceleration in AI edge applications
D Chen, Z Guo, J Fang, C Zhao, J Jiang… - … on Circuits and …, 2022 - ieeexplore.ieee.org
Resistive random-access memory (ReRAM) is widely studied in computing-in-memory (CIM)
for neural network acceleration in edge devices. However, the static current of conventional …
for neural network acceleration in edge devices. However, the static current of conventional …
A high-parallelism RRAM-based compute-in-memory macro with intrinsic impedance boosting and in-ADC computing
Resistive random access memory (RRAM) is considered to be a promising compute-in-
memory (CIM) platform; however, they tend to lose energy efficiency quickly in high …
memory (CIM) platform; however, they tend to lose energy efficiency quickly in high …
Pim-hls: An automatic hardware generation tool for heterogeneous processing-in-memory-based neural network accelerators
Processing-in-memory (PIM) architectures have shown great abilities for neural network
(NN) acceleration on edge devices that demand low latency under severe area constraints …
(NN) acceleration on edge devices that demand low latency under severe area constraints …