A variety of methods have been applied to the architectural configuration and learning or
training of artificial deep neural networks (DNN). These methods play a crucial role in the …

Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML
applications, the number of different ML inference systems has exploded. Over 100 …

We investigate a new method for injecting backdoors into machine learning models, based
on compromising the loss-value computation in the model-training code. We use it to …

Abstract Machine learning is experiencing an explosion of software and hardware solutions,
and needs industry-standard performance benchmarks to drive design and enable …

In computer architecture, significant innovation frequently comes from industry. However, the
simulation tools used by industry are often not released for open use, and even when they …

The widespread application of deep learning has changed the landscape of computation in
data centers. In particular, personalized recommendation for content ranking is now largely …

Neural personalized recommendation is the cornerstone of a wide collection of cloud
services and products, constituting significant compute demand of cloud infrastructure. Thus …

The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …