Skin-interfaced electronics is gradually changing medical practices by enabling continuous
and non-invasive tracking of physiological and biochemical information. With the rise of big …

With the down-scaling of CMOS technology, the design complexity of very large-scale
integrated is increasing. Although the application of machine learning (ML) techniques in …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ)
computers. Previous work for mitigating noise has primarily focused on gate-level or pulse …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous task in various
engineering and scientific applications. However, inner product based SpGEMM introduces …

We present APQ, a novel design methodology for efficient deep learning deployment. Unlike
previous methods that separately optimize the neural network architecture, pruning policy …

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

Fast and accurate pre-routing timing prediction is essential for timing-driven placement since
repetitive routing and static timing analysis (STA) iterations are expensive and …