Deep learning has been successful in automating the design of features in machine learning
pipelines. However, the algorithms optimizing neural network parameters remain largely …

Deep neural networks are commonly developed and trained in 32-bit floating point format.
Significant gains in performance and energy efficiency could be realized by training and …

Historically, progress in neural networks and deep learning research has been greatly
influenced by the available hardware and software tools. This paper identifies trends in deep …

Machine learning is becoming increasingly important in this era of big data. It enables us to
extract meaningful information from the overwhelming amount of data being generated and …

To realize the promise of ubiquitous embedded deep network inference, it is essential to
seek limits of energy and area efficiency. To this end, low-precision networks offer …

The recent ground-breaking advances in deep learning networks (DNNs) make them
attractive for embedded systems. However, it can take a long time for DNNs to make an …

Most commercial deep learning applications today use 32-bits of floating point precision
(𝑓𝑝32) for training and inference workloads. Various researchers have demonstrated that …

We present an approach to adaptively utilize deep neural networks in order to reduce the
evaluation time on new examples without loss of accuracy. Rather than attempting to …

The progress of some AI paradigms such as deep learning is said to be linked to an
exponential growth in the number of parameters. There are many studies corroborating …

The recent breakthrough in artificial intelligence (AI), especially deep neural networks
(DNNs), has affected every branch of science and technology. Particularly, edge AI has …