11,812 research outputs found
Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices
A recent trend in DNN development is to extend the reach of deep learning
applications to platforms that are more resource and energy constrained, e.g.,
mobile devices. These endeavors aim to reduce the DNN model size and improve
the hardware processing efficiency, and have resulted in DNNs that are much
more compact in their structures and/or have high data sparsity. These compact
or sparse models are different from the traditional large ones in that there is
much more variation in their layer shapes and sizes, and often require
specialized hardware to exploit sparsity for performance improvement. Thus,
many DNN accelerators designed for large DNNs do not perform well on these
models. In this work, we present Eyeriss v2, a DNN accelerator architecture
designed for running compact and sparse DNNs. To deal with the widely varying
layer shapes and sizes, it introduces a highly flexible on-chip network, called
hierarchical mesh, that can adapt to the different amounts of data reuse and
bandwidth requirements of different data types, which improves the utilization
of the computation resources. Furthermore, Eyeriss v2 can process sparse data
directly in the compressed domain for both weights and activations, and
therefore is able to improve both processing speed and energy efficiency with
sparse models. Overall, with sparse MobileNet, Eyeriss v2 in a 65nm CMOS
process achieves a throughput of 1470.6 inferences/sec and 2560.3 inferences/J
at a batch size of 1, which is 12.6x faster and 2.5x more energy efficient than
the original Eyeriss running MobileNet. We also present an analysis methodology
called Eyexam that provides a systematic way of understanding the performance
limits for DNN processors as a function of specific characteristics of the DNN
model and accelerator design; it applies these characteristics as sequential
steps to increasingly tighten the bound on the performance limits.Comment: accepted for publication in IEEE Journal on Emerging and Selected
Topics in Circuits and Systems. This extended version on arXiv also includes
Eyexam in the appendi
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
- …