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 …
Memristive crossbar arrays for storage and computing applications
The emergence of memristors with potential applications in data storage and artificial
intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with …
intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with …
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 …
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 …
Accurate deep neural network inference using computational phase-change memory
In-memory computing using resistive memory devices is a promising non-von Neumann
approach for making energy-efficient deep learning inference hardware. However, due to …
approach for making energy-efficient deep learning inference hardware. However, due to …