Mobile and wearable devices have enabled numerous applications, including activity
tracking, wellness monitoring, and human–computer interaction, that measure and improve …

As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …

We present a recurrent neural network (RNN) accelerator design with resistive random-
access memory (ReRAM)-based processing-in-memory (PIM) architecture. Distinguished …

Processing-in-memory (PIM) is a promising architecture to design various types of neural
network accelerators as it ensures the efficiency of computation together with Resistive …

Crossbar architecture has been widely adopted in neural network accelerators due to the
efficient implementations on vector-matrix multiplication operations. However, in the case of …

Processing-in-memory (PIM) architecture based on resistive random access memory
(ReRAM) crossbars is a promising solution to the memory bottleneck that long short-term …

Training of neural network can be accelerated by limited numerical precision together with
specialized low-precision hardware. This paper studies how low precision can impact on …

Processing-in-memory (PIM) enables energy-efficient deployment of convolutional neural
networks (CNNs) from edge to cloud. Resistive random-access memory (ReRAM) is one of …

This paper presents a survey of the currently available hardware designs for implementation
of the human cortex inspired algorithm, Hierarchical Temporal Memory (HTM). In this review …

As computational models inspired by the biological neural system, spiking neural networks
(SNN) continue to demonstrate great potential in the landscape of artificial intelligence …