130,930 research outputs found
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations
Quantization of Convolutional Neural Networks (CNNs) is a common approach to
ease the computational burden involved in the deployment of CNNs, especially on
low-resource edge devices. However, fixed-point arithmetic is not natural to
the type of computations involved in neural networks. In this work, we explore
ways to improve quantized CNNs using PDE-based perspective and analysis. First,
we harness the total variation (TV) approach to apply edge-aware smoothing to
the feature maps throughout the network. This aims to reduce outliers in the
distribution of values and promote piece-wise constant maps, which are more
suitable for quantization. Secondly, we consider symmetric and stable variants
of common CNNs for image classification, and Graph Convolutional Networks
(GCNs) for graph node-classification. We demonstrate through several
experiments that the property of forward stability preserves the action of a
network under different quantization rates. As a result, stable quantized
networks behave similarly to their non-quantized counterparts even though they
rely on fewer parameters. We also find that at times, stability even aids in
improving accuracy. These properties are of particular interest for sensitive,
resource-constrained, low-power or real-time applications like autonomous
driving
Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics
We present a design methodology that enables the semi-automatic generation of
a hardware-accelerated graph building architectures for locally constrained
graphs based on formally described detector definitions. In addition, we define
a similarity measure in order to compare our locally constrained graph building
approaches with commonly used k-nearest neighbour building approaches. To
demonstrate the feasibility of our solution for particle physics applications,
we implemented a real-time graph building approach in a case study for the
Belle~II central drift chamber using Field-Programmable Gate Arrays~(FPGAs).
Our presented solution adheres to all throughput and latency constraints
currently present in the hardware-based trigger of the Belle~II experiment. We
achieve constant time complexity at the expense of linear space complexity and
thus prove that our automated methodology generates online graph building
designs suitable for a wide range of particle physics applications. By enabling
an hardware-accelerated pre-processing of graphs, we enable the deployment of
novel Graph Neural Networks~(GNNs) in first level triggers of particle physics
experiments.Comment: 18 page
ViGEO: an Assessment of Vision GNNs in Earth Observation
Satellite missions and Earth Observation (EO) systems represent fundamental
assets for environmental monitoring and the timely identification of
catastrophic events, long-term monitoring of both natural resources and
human-made assets, such as vegetation, water bodies, forests as well as
buildings. Different EO missions enables the collection of information on
several spectral bandwidths, such as MODIS, Sentinel-1 and Sentinel-2. Thus,
given the recent advances of machine learning, computer vision and the
availability of labeled data, researchers demonstrated the feasibility and the
precision of land-use monitoring systems and remote sensing image
classification through the use of deep neural networks. Such systems may help
domain experts and governments in constant environmental monitoring, enabling
timely intervention in case of catastrophic events (e.g., forest wildfire in a
remote area). Despite the recent advances in the field of computer vision, many
works limit their analysis on Convolutional Neural Networks (CNNs) and, more
recently, to vision transformers (ViTs). Given the recent successes of Graph
Neural Networks (GNNs) on non-graph data, such as time-series and images, we
investigate the performances of a recent Vision GNN architecture (ViG) applied
to the task of land cover classification. The experimental results show that
ViG achieves state-of-the-art performances in multiclass and multilabel
classification contexts, surpassing both ViT and ResNet on large-scale
benchmarks.Comment: Accepted at SSTDM 2023 workshop, held in conjunction with ICDM 2023
conferenc
Community detection with spiking neural networks for neuromorphic hardware
We present results related to the performance of an algorithm for community
detection which incorporates event-driven computation. We define a mapping
which takes a graph G to a system of spiking neurons. Using a fully connected
spiking neuron system, with both inhibitory and excitatory synaptic
connections, the firing patterns of neurons within the same community can be
distinguished from firing patterns of neurons in different communities. On a
random graph with 128 vertices and known community structure we show that by
using binary decoding and a Hamming-distance based metric, individual
communities can be identified from spike train similarities. Using bipolar
decoding and finite rate thresholding, we verify that inhibitory connections
prevent the spread of spiking patterns.Comment: Conference paper presented at ORNL Neuromorphic Workshop 2017, 7
pages, 6 figure
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