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 …

Deep neural network (DNN) accelerators as an example of domain-specific architecture
have demonstrated great success in DNN inference. However, the architecture acceleration …

Recent years have witnessed a significant interest in the “generative adversarial
networks”(GANs) due to their ability to generate high-fidelity data. Many models of GANs …

The invention of Transformer model structure boosts the performance of Neural Machine
Translation (NMT) tasks to an unprecedented level. Many previous works have been done to …

Abstract Generative Adversarial Network (GAN) requires extensive computing resources
making its implementation in edge devices with conventional microprocessor hardware a …

This paper first presents an input-stationary (IS) implemented crossbar accelerator (INCA),
supporting inference and training for deep neural networks (DNNs). Processing-in-memory …

Technological advances in long read sequences have greatly facilitated the development of
genomics. However, managing and analyzing the raw genomic data that outpaces Moore's …

Due to the need for computing models that can process large quantities of data efficiently
and with high throughput in many state-of-the-art machine learning algorithms, the …

Generative adversarial networks (GANs) have shown excellent performance in image and
speech applications. GANs create impressive data primarily through a new type of operator …

Resistive random access memory (ReRAM) has been proven capable to efficiently perform
in-situ matrix-vector computations in convolutional neural network (CNN) processing. The …