Hardware Acceleration of Adaptive Neural Algorithms.

Technical Report · Wed Nov 01 04:00:00 EDT 2017

DOI:https://doi.org/10.2172/1410778· OSTI ID:1410778

James, Conrad D. ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1410778
• Report Number(s): SAND--2017-12810; 659046
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING