The viability of analog-based accelerators for neuromorphic computing: a survey
M Musisi-Nkambwe, S Afshari, H Barnaby… - Neuromorphic …, 2021 - iopscience.iop.org
Focus in deep neural network hardware research for reducing latencies of memory fetches
has steered in the direction of analog-based artificial neural networks (ANN). The promise of …
has steered in the direction of analog-based artificial neural networks (ANN). The promise of …
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 …
Nonvolatile Capacitive Crossbar Array for In‐Memory Computing
Conventional resistive crossbar array for in‐memory computing suffers from high static
current/power, serious IR drop, and sneak paths. In contrast, the “capacitive” crossbar array …
current/power, serious IR drop, and sneak paths. In contrast, the “capacitive” crossbar array …
Nonvolatile capacitive synapse: device candidates for charge domain compute-in-memory
Compute-in-memory (CIM) has emerged as a compelling approach to address the ever-
increasing demand for energy-efficient computing for edge artificial intelligence (AI) …
increasing demand for energy-efficient computing for edge artificial intelligence (AI) …
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 …
H3datten: Heterogeneous 3-d integrated hybrid analog and digital compute-in-memory accelerator for vision transformer self-attention
After the success of the transformer networks on natural language processing (NLP), the
application of transformers to computer vision (CV) has followed suit to deliver …
application of transformers to computer vision (CV) has followed suit to deliver …
Role of the electrolyte layer in CMOS-compatible and oxide-based vertical three-terminal ECRAM
G Han, J Seo, H Kim, D Lee - Journal of Materials Chemistry C, 2023 - pubs.rsc.org
Structured three-terminal electrochemical random access memory (3T-ECRAM) is
developed as a synaptic device at wafer scale using CMOS fabrication-compatible …
developed as a synaptic device at wafer scale using CMOS fabrication-compatible …
OCC: An automated end-to-end machine learning optimizing compiler for computing-in-memory
Memristive devices promise an alternative approach toward non-Von Neumann
architectures, where specific computational tasks are performed within the memory devices …
architectures, where specific computational tasks are performed within the memory devices …
Memory-immersed collaborative digitization for area-efficient compute-in-memory deep learning
This work discusses memory-immersed collaborative digitization among compute-in-
memory (CiM) arrays to minimize the area overheads of a conventional analog-to-digital …
memory (CiM) arrays to minimize the area overheads of a conventional analog-to-digital …
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 …