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Craig M. Vineyard
Researcher at Sandia National Laboratories
Publications - 39
Citations - 376
Craig M. Vineyard is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Artificial neural network & Neuromorphic engineering. The author has an hindex of 7, co-authored 33 publications receiving 237 citations. Previous affiliations of Craig M. Vineyard include University of New Mexico.
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Proceedings ArticleDOI
Neurogenesis deep learning: Extending deep networks to accommodate new classes
Timothy J. Draelos,Nadine E. Miner,Christopher C. Lamb,Jonathan A. Cox,Craig M. Vineyard,Kristofor D. Carlson,William Severa,Conrad D. James,James B. Aimone +8 more
TL;DR: In this article, the authors explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations.
Journal ArticleDOI
A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications
Conrad D. James,James B. Aimone,Nadine E. Miner,Craig M. Vineyard,Fredrick Rothganger,Kristofor D. Carlson,Samuel A. Mulder,Timothy J. Draelos,Aleksandra Faust,Aleksandra Faust,Matthew J. Marinella,John H. Naegle,Steven J. Plimpton +12 more
TL;DR: Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.
Journal ArticleDOI
Training deep neural networks for binary communication with the Whetstone method
TL;DR: Whetstone as mentioned in this paper is a method to bridge the gap by converting deep neural networks to have discrete, binary communication, where activation function at each layer is progressively sharpened towards a threshold activation, with limited loss in performance.
Journal ArticleDOI
Computing with Spikes: The Advantage of Fine-Grained Timing.
Stephen J. Verzi,Fredrick Rothganger,Ojas Parekh,Tu-Thach Quach,Nadine E. Miner,Craig M. Vineyard,Conrad D. James,James B. Aimone +7 more
TL;DR: It is demonstrated that spike-based communication and computation within algorithms can increase throughput, and they can decrease energy cost in some cases.
Journal ArticleDOI
Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence
Frances S. Chance,James B. Aimone,Srideep Musuvathy,Michael R. Smith,Craig M. Vineyard,Felix Wang +5 more
TL;DR: Cultural differences between the two fields are discussed, including divergent priorities that should be considered when leveraging modern-day neuroscience for AI and small but significant cultural shifts that would greatly facilitate increased synergy between theTwo fields are highlighted.