[HTML][HTML] An analog-AI chip for energy-efficient speech recognition and transcription
Abstract Models of artificial intelligence (AI) that have billions of parameters can achieve
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …
high accuracy across a range of tasks,, but they exacerbate the poor energy efficiency of …
Programming memristor arrays with arbitrarily high precision for analog computing
In-memory computing represents an effective method for modeling complex physical
systems that are typically challenging for conventional computing architectures but has been …
systems that are typically challenging for conventional computing architectures but has been …
A 28-nm RRAM computing-in-memory macro using weighted hybrid 2T1R cell array and reference subtracting sense amplifier for AI edge inference
W Ye, L Wang, Z Zhou, J An, W Li… - IEEE Journal of Solid …, 2023 - ieeexplore.ieee.org
Non-volatile computing-in-memory (nvCIM) can potentially meet the ever-increasing
demands on improving the energy efficiency (EF) for intelligent edge devices. However, it …
demands on improving the energy efficiency (EF) for intelligent edge devices. However, it …
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) …
A 22 nm Floating-Point ReRAM Compute-in-Memory Macro Using Residue-Shared ADC for AI Edge Device
Artificial intelligence (AI) edge devices increasingly require the enhanced accuracy of
floating-point (FP) multiply-and-accumulate (MAC) operations as well as nonvolatile on-chip …
floating-point (FP) multiply-and-accumulate (MAC) operations as well as nonvolatile on-chip …
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 …
[HTML][HTML] Roadmap on low-power electronics
This article is written on behalf of many colleagues, collaborators, and researchers in the
field of advanced materials who continue to enable and undertake cutting-edge research in …
field of advanced materials who continue to enable and undertake cutting-edge research in …
Hardware/software co-design with adc-less in-memory computing hardware for spiking neural networks
Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for
realizing energy-efficient implementations of sequential tasks on resource-constrained edge …
realizing energy-efficient implementations of sequential tasks on resource-constrained edge …
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 …
An RRAM-based digital computing-in-memory macro with dynamic voltage sense amplifier and sparse-aware approximate adder tree
RRAM is a promising candidate to implement large-capacity in-memory computing on edge
AI devices due to its high density. However, the efficiency and accuracy of RRAM-based …
AI devices due to its high density. However, the efficiency and accuracy of RRAM-based …