2 research outputs found
High Performance Computing for Geospatial Applications: A Prospective View
The pace of improvement in the performance of conventional computer hardware
has slowed significantly during the past decade, largely as a consequence of
reaching the physical limits of manufacturing processes. To offset this
slowdown, new approaches to HPC are now undergoing rapid development. This
chapter describes current work on the development of cutting-edge exascale
computing systems that are intended to be in place in 2021 and then turns to
address several other important developments in HPC, some of which are only in
the early stage of development. Domain-specific heterogeneous processing
approaches use hardware that is tailored to specific problem types.
Neuromorphic systems are designed to mimic brain function and are well suited
to machine learning. And then there is quantum computing, which is the subject
of some controversy despite the enormous funding initiatives that are in place
to ensure that systems continue to scale-up from current small demonstration
systems.Comment: Forthcoming in W. Tang and S. Wang (eds.) High-Performance Computing
for Geospatial Applications, Springe
A mixed signal architecture for convolutional neural networks
Deep neural network (DNN) accelerators with improved energy and delay are
desirable for meeting the requirements of hardware targeted for IoT and edge
computing systems. Convolutional neural networks (CoNNs) belong to one of the
most popular types of DNN architectures. This paper presents the design and
evaluation of an accelerator for CoNNs. The system-level architecture is based
on mixed-signal, cellular neural networks (CeNNs). Specifically, we present (i)
the implementation of different layers, including convolution, ReLU, and
pooling, in a CoNN using CeNN, (ii) modified CoNN structures with CeNN-friendly
layers to reduce computational overheads typically associated with a CoNN,
(iii) a mixed-signal CeNN architecture that performs CoNN computations in the
analog and mixed signal domain, and (iv) design space exploration that
identifies what CeNN-based algorithm and architectural features fare best
compared to existing algorithms and architectures when evaluated over common
datasets -- MNIST and CIFAR-10. Notably, the proposed approach can lead to
8.7 improvements in energy-delay product (EDP) per digit classification
for the MNIST dataset at iso-accuracy when compared with the state-of-the-art
DNN engine, while our approach could offer 4.3 improvements in EDP when
compared to other network implementations for the CIFAR-10 dataset.Comment: 25 page