[HTML][HTML] 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 …
In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and …
The low communication bandwidth between memory and processing units in conventional
von Neumann machines does not support the requirements of emerging applications that …
von Neumann machines does not support the requirements of emerging applications that …
A four-megabit compute-in-memory macro with eight-bit precision based on CMOS and resistive random-access memory for AI edge devices
JM Hung, CX Xue, HY Kao, YH Huang, FC Chang… - Nature …, 2021 - nature.com
Non-volatile computing-in-memory (nvCIM) architecture can reduce the latency and energy
consumption of artificial intelligence computation by minimizing the movement of data …
consumption of artificial intelligence computation by minimizing the movement of data …
IntAct: A 96-core processor with six chiplets 3D-stacked on an active interposer with distributed interconnects and integrated power management
P Vivet, E Guthmuller, Y Thonnart… - IEEE Journal of Solid …, 2020 - ieeexplore.ieee.org
In the context of high-performance computing, the integration of more computing capabilities
with generic cores or dedicated accelerators for artificial intelligence (AI) application is …
with generic cores or dedicated accelerators for artificial intelligence (AI) application is …
[HTML][HTML] Advances in emerging memory technologies: From data storage to artificial intelligence
G Molas, E Nowak - Applied Sciences, 2021 - mdpi.com
This paper presents an overview of emerging memory technologies. It begins with the
presentation of stand-alone and embedded memory technology evolution, since the …
presentation of stand-alone and embedded memory technology evolution, since the …
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 …
Multi-state memristors and their applications: An overview
Memristors show great potential for being integrated into CMOS technology and provide
new approaches for designing computing-in-memory (CIM) systems, brain-inspired …
new approaches for designing computing-in-memory (CIM) systems, brain-inspired …
CHIMERA: A 0.92-TOPS, 2.2-TOPS/W edge AI accelerator with 2-MByte on-chip foundry resistive RAM for efficient training and inference
Implementing edge artificial intelligence (AI) inference and training is challenging with
current memory technologies. As deep neural networks (DNNs) grow in size, this problem is …
current memory technologies. As deep neural networks (DNNs) grow in size, this problem is …
Defects, fault modeling, and test development framework for RRAMs
Resistive RAM (RRAM) is a promising technology to replace traditional technologies such
as Flash, because of its low energy consumption, CMOS compatibility, and high density …
as Flash, because of its low energy consumption, CMOS compatibility, and high density …
On-chip memory technology design space explorations for mobile deep neural network accelerators
Deep neural network (DNN) inference tasks have become ubiquitous workloads on mobile
SoCs and demand energy-efficient hardware accelerators. Mobile DNN accelerators are …
SoCs and demand energy-efficient hardware accelerators. Mobile DNN accelerators are …