262 research outputs found
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mlGeNN: accelerating SNN inference using GPU-enabled neural networks
In this paper we present mlGeNN – a Python library for the conversion of artificial neural networks (ANNs) specified in Keras to spiking neural networks (SNNs). SNNs are simulated using GeNN with extensions to efficiently support convolutional connectivity and batching. We evaluate converted SNNs on CIFAR-10 and ImageNet classification tasks and compare the performance to both the original ANNs and other SNN simulators. We find that performing inference using a VGG-16 model, trained on the CIFAR-10 dataset, is 2.5× faster than BindsNet and, when using a ResNet-20 model trained on CIFAR-10 with FewSpike ANN to SNN conversion, mlGeNN is only a little over 2× slower than TensorFlow
2015 Annual Research Symposium Abstract Book
2015 annual volume of abstracts for science research projects conducted by students at Trinity Colleg
Deep Representation Learning and Prediction for Forest Wildfires
An average of 8000 forest wildfires occurs each year in Canada burning an average of 2.5M ha/year as reported by the Government of Canada. Given the current rate of climate change, this number is expected to increase each year. Being able to predict how the fires spread would play a critical role in fire risk management. However, given the complexity of the natural processes that influence a fire system, most of the models used for simulating wildfires are computationally expensive and need a high variety of information about the environmental parameters to be able to give good performances. Deep learning algorithms allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of its relation to simpler concepts. We propose a deep learning predictor that uses a Deep Convolutional Auto-Encoder to learn the key structures of a forest wildfire spread from images and a Long Short Term Memory to predict the next phase of the fire. We divided the predictor problem in three phases: find a dataset of wildfires, learning the essential structure of forest fire, and predict the next image. We first present the simulated wildfires dataset and the algorithm we applied on it to make it more suitable to the model. Then we present the Deep Forest Wildfire Auto-Encoder and its implementation using the Caffe framework. Particular attention is given to the design considerations and to the best practice used to implement the model. We also present the design of the Deep Forest Wildfire Predictor, and some possible future variations of it
Hardware/Software co-design with ADC-Less In-memory Computing Hardware for Spiking Neural Networks
Spiking Neural Networks (SNNs) are bio-plausible models that hold great
potential for realizing energy-efficient implementations of sequential tasks on
resource-constrained edge devices. However, commercial edge platforms based on
standard GPUs are not optimized to deploy SNNs, resulting in high energy and
latency. While analog In-Memory Computing (IMC) platforms can serve as
energy-efficient inference engines, they are accursed by the immense energy,
latency, and area requirements of high-precision ADCs (HP-ADC), overshadowing
the benefits of in-memory computations. We propose a hardware/software
co-design methodology to deploy SNNs into an ADC-Less IMC architecture using
sense-amplifiers as 1-bit ADCs replacing conventional HP-ADCs and alleviating
the above issues. Our proposed framework incurs minimal accuracy degradation by
performing hardware-aware training and is able to scale beyond simple image
classification tasks to more complex sequential regression tasks. Experiments
on complex tasks of optical flow estimation and gesture recognition show that
progressively increasing the hardware awareness during SNN training allows the
model to adapt and learn the errors due to the non-idealities associated with
ADC-Less IMC. Also, the proposed ADC-Less IMC offers significant energy and
latency improvements, and , respectively, depending
on the SNN model and the workload, compared to HP-ADC IMC.Comment: 12 pages, 13 figure
Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications
[Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on
neuromorphic chips with high energy efficiency by introducing neural dynamics
and spike properties. As the emerging spiking deep learning paradigm attracts
increasing interest, traditional programming frameworks cannot meet the demands
of the automatic differentiation, parallel computation acceleration, and high
integration of processing neuromorphic datasets and deployment. In this work,
we present the SpikingJelly framework to address the aforementioned dilemma. We
contribute a full-stack toolkit for pre-processing neuromorphic datasets,
building deep SNNs, optimizing their parameters, and deploying SNNs on
neuromorphic chips. Compared to existing methods, the training of deep SNNs can
be accelerated , and the superior extensibility and flexibility of
SpikingJelly enable users to accelerate custom models at low costs through
multilevel inheritance and semiautomatic code generation. SpikingJelly paves
the way for synthesizing truly energy-efficient SNN-based machine intelligence
systems, which will enrich the ecology of neuromorphic computing.Comment: Accepted in Science Advances
(https://www.science.org/doi/10.1126/sciadv.adi1480
QES-Fire: A dynamically coupled fast-response wildfire model
A microscale wildfire model, QES-Fire, that dynamically couples the fire front to microscale winds was developed using a simplified physics rate of spread (ROS) model, a kinematic plume-rise model and a mass-consistent wind solver. The model is three-dimensional and couples fire heat fluxes to the wind field while being more computationally efficient than other coupled models. The plume-rise model calculates a potential velocity field scaled by the ROS model\u27s fire heat flux. Distinct plumes are merged using a multiscale plume-merging methodology that can efficiently represent complex fire fronts. The plume velocity is then superimposed on the ambient winds and the wind solver enforces conservation of mass on the combined field, which is then fed into the ROS model and iterated on until convergence. QES-Fire\u27s ability to represent plume rise is evaluated by comparing its results with those from an atmospheric large-eddy simulation (LES) model. Additionally, the model is compared with data from the FireFlux II field experiment. QES-Fire agrees well with both the LES and field experiment data, with domain-integrated buoyancy fluxes differing by less than 17% between LES and QES-Fire and less than a 10% difference in the ROS between QES-Fire and FireFlux II data
Libro de Actas JCC&BD 2018 : VI Jornadas de Cloud Computing & Big Data
Se recopilan las ponencias presentadas en las VI Jornadas de Cloud Computing & Big Data (JCC&BD), realizadas entre el 25 al 29 de junio de 2018 en la Facultad de Informática de la Universidad Nacional de La Plata.Universidad Nacional de La Plata (UNLP) - Facultad de Informátic
Chemistry Across Multiple Phases (CAMP) version 1.0: an integrated multiphase chemistry model
A flexible treatment for gas- and aerosol-phase chemical processes has been developed for models of diverse scale, from box models up to global models. At the core of this novel framework is an “abstracted aerosol representation” that allows a given chemical mechanism to be solved in atmospheric models with different aerosol representations (e.g., sectional, modal, or particle-resolved). This is accomplished by treating aerosols as a collection of condensed phases that are implemented according to the aerosol representation of the host model. The framework also allows multiple chemical processes (e.g., gas- and aerosol-phase chemical reactions, emissions, deposition, photolysis, and mass transfer) to be solved simultaneously as a single system. The flexibility of the model is achieved by (1) using an object-oriented design that facilitates extensibility to new types of chemical processes and to new ways of representing aerosol systems, (2) runtime model configuration using JSON input files that permits making changes to any part of the chemical mechanism without recompiling the model (this widely used, human-readable format allows entire gas- and aerosol-phase chemical mechanisms to be described with as much complexity as necessary), and (3) automated comprehensive testing that ensures stability of the code as new functionality is introduced. Together, these design choices enable users to build a customized multiphase mechanism without having to handle preprocessors, solvers, or compilers. Removing these hurdles makes this type of modeling accessible to a much wider community, including modelers, experimentalists, and educators. This new treatment compiles as a stand-alone library and has been deployed in the particle-resolved PartMC model and in the Multiscale Online AtmospheRe CHemistry (MONARCH) chemical weather prediction system for use at regional and global scales. Results from the initial deployment to box models of different complexity and MONARCH will be discussed, along with future extension to more complex gas–aerosol systems and the integration of GPU-based solvers.Matthew L. Dawson has received funding from the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 747048. Matthew L. Dawson, Oriol Jorba, and Christian Guzman have been supported by the Ministerio de Ciencia, Innovación y Universidades (grant no. RTI2018-099894-BI00). Christian Guzman acknowledges funding from the AXA Research Fund. Nicole Riemer, Matthew West, and Jeffrey H. Curtis acknowledge funding from the National Science Foundation (grant no. AGS 19-41110). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under cooperative agreement no. 1852977.Peer ReviewedPostprint (published version
Chemistry Across Multiple Phases (CAMP) version 1.0: an integrated multiphase chemistry model
A flexible treatment for gas- and aerosol-phase chemical processes has been developed for models of diverse scale, from box models up to global models. At the core of this novel framework is an “abstracted aerosol representation” that allows a given chemical mechanism to be solved in atmospheric models with different aerosol representations (e.g., sectional, modal, or particle-resolved). This is accomplished by treating aerosols as a collection of condensed phases that are implemented according to the aerosol representation of the host model. The framework also allows multiple chemical processes (e.g., gas- and aerosol-phase chemical reactions, emissions, deposition, photolysis, and mass transfer) to be solved simultaneously as a single system. The flexibility of the model is achieved by (1) using an object-oriented design that facilitates extensibility to new types of chemical processes and to new ways of representing aerosol systems, (2) runtime model configuration using JSON input files that permits making changes to any part of the chemical mechanism without recompiling the model (this widely used, human-readable format allows entire gas- and aerosol-phase chemical mechanisms to be described with as much complexity as necessary), and (3) automated comprehensive testing that ensures stability of the code as new functionality is introduced. Together, these design choices enable users to build a customized multiphase mechanism without having to handle preprocessors, solvers, or compilers. Removing these hurdles makes this type of modeling accessible to a much wider community, including modelers, experimentalists, and educators. This new treatment compiles as a stand-alone library and has been deployed in the particle-resolved PartMC model and in the Multiscale Online AtmospheRe CHemistry (MONARCH) chemical weather prediction system for use at regional and global scales. Results from the initial deployment to box models of different complexity and MONARCH will be discussed, along with future extension to more complex gas–aerosol systems and the integration of GPU-based solvers.Matthew L. Dawson has received funding from the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 747048. Matthew L. Dawson, Oriol Jorba, and Christian Guzman have been supported by the Ministerio de Ciencia, Innovación y Universidades (grant no. RTI2018-099894-BI00). Christian Guzman acknowledges funding from the AXA Research Fund. Nicole Riemer, Matthew West, and Jeffrey H. Curtis acknowledge funding from the National Science Foundation (grant no. AGS 19-41110). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under cooperative agreement no. 1852977.Peer ReviewedPostprint (published version
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