Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

While vision transformers (ViTs) have continuously achieved new milestones in the field of
computer vision, their sophisticated network architectures with high computation and …

Transformer-based Large Language Models (LLMs) have made a significant impact on
various domains. However, LLMs' efficiency suffers from both heavy computation and …

Reading a qubit is a fundamental operation in quantum computing. It translates quantum
information into classical information enabling subsequent classification to assign the qubit …

We present a DNN accelerator that allows inference at arbitrary precision with dedicated
processing elements that are configurable at the bit level. Our DNN accelerator has 8 …

Convolutional neural networks (CNNs) have been widely adopted for various machine
intelligence tasks. Nevertheless, CNNs are still known to be computational demanding due …

Binary neural network (BNN), where both the weight and the activation values are
represented with one bit, provides an attractive alternative to deploy highly efficient deep …

Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for
a deep learning task promise significant advantages of both inference accuracy and …

Machine learning (ML) accelerators have been studied and used extensively to compute ML
models with high performance and low power. However, designing such accelerators …

Stochastic rounding is a critical technique used in low-precision deep neural networks
(DNNs) training to ensure good model accuracy. However, it requires a large number of …