DANNA: A neuromorphic software ecosystem

Disney, Adam; Reynolds, John J.; Schuman, Catherine D.; Klibisz, Aleksander; Young, Aaron; Plank, James S.

doi:10.1016/j.bica.2016.07.007

Cited by 14 publications

(11 citation statements)

References 6 publications

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“…In AER communication, each neuron has a unique address, and when a spike is generated that will traverse between chips, the address specifies to which chip it will go. Custom PCI Example neuromorphic visualization tools, giving a high-level view of a spiking neural network model [2341] and a low-level view of a network layout on a particular neuromorphic implementation [2342].…”

Section: A Communicationmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

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172

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Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brainlike ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

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Section: A Communicationmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

Self Cite

172

180

View full text Add to dashboard Cite

show abstract

“…Many well-known deep learning models, such as VGGNet [37], GoogLeNet [38], and Microsoft ResNet [39], have achieved excellent results in international classification recognition competitions, and they offer many ways to solve problems. In this study, we compare the proposed 2-CLSTM Model with VGGNet, GoogLeNet, and ResNet50 using the same input data set and Adam [40] optimizer. VGGNet's structure is simple, and it bears a lot of similarities to the traditional CNN model.…”

Section: E Baseline Modelmentioning

confidence: 99%

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Cheng

Chen

et al. 2020

IEEE Access

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Atrial fibrillation (AF) is the most common persistent arrhythmia and is likely to cause strokes and damage to heart function in patients. Electrocardiogram (ECG) is the gold standard for detecting AF. However, ECGs have short boards with short monitoring cycles and problems with gathering. It is also difficult to detect a burst AF through ECG. In contrast, photoplethysmography (PPG) is easy to perform and suitable for long-term monitoring. In this study, we propose a method that combines timefrequency analysis with deep learning and identifies AF based on PPG. The advantage of the method is that there is no need for the noise filtering and feature extraction of PPG, and it has a high generalization capability. The data for the experiment came from three publicly accessible databases. The first part of the experimental method uses data augmentation to convert the 10 s PPG segment into a time-frequency chromatograph by means of time-frequency analysis. The second part inputs the chromatograph into a hybrid framework that combines a convolutional neural network (CNN) and long short-term memory (LSTM) for AF/nonAF classification. The experimental results show that the method has a high classification accuracy, sensitivity, specificity, and F1 score, which are equal to 98.21%, 98.00%, 98.07% and 98.13%, respectively. The area under the receiver operating characteristic curve (AUC) is 0.9959. The model we propose not only aids doctors in diagnosing AF but also provides a method for identifying AF through portable wearable devices. INDEX TERMS Atrial fibrillation, photoplethysmography (PPG), time-frequency analysis, convolutional neural networks (CNN), long short-term memory (LSTM).

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“…Some of the primary difficulties include: (1) Neuromorphic chips are a niche product and difficult to procure at volume; (2) There is insufficient software interfaces for developing applications; (3) Many algorithms are incompatible or may underperform on neuromorphic hardware; (4) Large-scale applications are often too large; (5) Cross-compiling code and I/O require considerable time and bandwidth from a host machine. See Diamond et al (2016), Severa et al (2019), Hunsberger and Eliasmith (2016), Disney et al (2016), Davison et al (2009), Ehsan et al (2017), and Wolfe et al (2018) for more details and possible approaches toward solving these challenges. Moreover, some of the themes from the Existence proof, human brains as efficient energy consumers section (e.g., minimizing wiring, keeping firing rates constant, using sparse and reduced representations) could be incorporated into neuromorphic designs.…”

Section: Current State Of Ai As It Pertains To Energy Consumptionmentioning

confidence: 99%

Making BREAD: Biomimetic Strategies for Artificial Intelligence Now and in the Future

et al. 2019

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The Artificial Intelligence (AI) revolution foretold of during the 1960s is well underway in the second decade of the twenty first century. Its period of phenomenal growth likely lies ahead. AI-operated machines and technologies will extend the reach of Homo sapiens far beyond the biological constraints imposed by evolution: outwards further into deep space, as well as inwards into the nano-world of DNA sequences and relevant medical applications. And yet, we believe, there are crucial lessons that biology can offer that will enable a prosperous future for AI. For machines in general, and for AI's especially, operating over extended periods or in extreme environments will require energy usage orders of magnitudes more efficient than exists today. In many operational environments, energy sources will be constrained. The AI's design and function may be dependent upon the type of energy source, as well as its availability and accessibility. Any plans for AI devices operating in a challenging environment must begin with the question of how they are powered, where fuel is located, how energy is stored and made available to the machine, and how long the machine can operate on specific energy units. While one of the key advantages of AI use is to reduce the dimensionality of a complex problem, the fact remains that some energy is required for functionality. Hence, the materials and technologies that provide the needed energy represent a critical challenge toward future use scenarios of AI and should be integrated into their design. Here we look to the brain and other aspects of biology as inspiration for Biomimetic Research for Energy-efficient AI Designs (BREAD).

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DANNA: A neuromorphic software ecosystem

Cited by 14 publications

References 6 publications

A Survey of Neuromorphic Computing and Neural Networks in Hardware

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Making BREAD: Biomimetic Strategies for Artificial Intelligence Now and in the Future

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