Recent advances and future prospects for memristive materials, devices, and systems
Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …
CMOS technology, which is facing fundamental limitations in its development. Since oxide …
The physics of optical computing
PL McMahon - Nature Reviews Physics, 2023 - nature.com
There has been a resurgence of interest in optical computing since the early 2010s, both in
academia and in industry, with much of the excitement centred around special-purpose …
academia and in industry, with much of the excitement centred around special-purpose …
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Analogue in-memory computing (AIMC) with resistive memory devices could reduce the
latency and energy consumption of deep neural network inference tasks by directly …
latency and energy consumption of deep neural network inference tasks by directly …
Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware
Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …
attention lately due to its promise of reducing the computational energy, latency, as well as …
[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 …
Hardware implementation of memristor-based artificial neural networks
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …
techniques, which rely on networks of connected simple computing units operating in …
A large-scale integrated vector–matrix multiplication processor based on monolayer molybdenum disulfide memories
Data-driven algorithms—such as signal processing and artificial neural networks—are
required to process and extract meaningful information from the massive amounts of data …
required to process and extract meaningful information from the massive amounts of data …
A survey on deep learning hardware accelerators for heterogeneous hpc platforms
Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …
solution for several classes of high-performance computing (HPC) applications such as …
A charge domain SRAM compute-in-memory macro with C-2C ladder-based 8-bit MAC unit in 22-nm FinFET process for edge inference
Compute-in-memory (CiM) is one promising solution to address the memory bottleneck
existing in traditional computing architectures. However, the tradeoff between energy …
existing in traditional computing architectures. However, the tradeoff between energy …
Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks
Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from
limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive …
limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive …