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 …
Neuro-inspired computing chips
The rapid development of artificial intelligence (AI) demands the rapid development of
domain-specific hardware specifically designed for AI applications. Neuro-inspired …
domain-specific hardware specifically designed for AI applications. Neuro-inspired …
Memory devices and applications for in-memory computing
Traditional von Neumann computing systems involve separate processing and memory
units. However, data movement is costly in terms of time and energy and this problem is …
units. However, data movement is costly in terms of time and energy and this problem is …
Compute-in-memory chips for deep learning: Recent trends and prospects
Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …
problem in hardware accelerator design for deep learning. The input vector and weight …
Resistive switching materials for information processing
The rapid increase in information in the big-data era calls for changes to information-
processing paradigms, which, in turn, demand new circuit-building blocks to overcome the …
processing paradigms, which, in turn, demand new circuit-building blocks to overcome the …
Skyrmion-based artificial synapses for neuromorphic computing
Magnetic skyrmions are topologically protected spin textures that have nanoscale
dimensions and can be manipulated by an electric current. These properties make the …
dimensions and can be manipulated by an electric current. These properties make the …
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …
deep learning workloads—computes matrix-vector multiplications but only approximately …
Open-loop analog programmable electrochemical memory array
P Chen, F Liu, P Lin, P Li, Y Xiao, B Zhang… - Nature …, 2023 - nature.com
Emerging memories have been developed as new physical infrastructures for hosting neural
networks owing to their low-power analog computing characteristics. However, accurately …
networks owing to their low-power analog computing characteristics. However, accurately …
Nanosecond protonic programmable resistors for analog deep learning
Nanoscale ionic programmable resistors for analog deep learning are 1000 times smaller
than biological cells, but it is not yet clear how much faster they can be relative to neurons …
than biological cells, but it is not yet clear how much faster they can be relative to neurons …
CMOS-compatible electrochemical synaptic transistor arrays for deep learning accelerators
In-memory computing architectures based on memristive crossbar arrays could offer higher
computing efficiency than traditional hardware in deep learning applications. However, the …
computing efficiency than traditional hardware in deep learning applications. However, the …