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His specialties are Computational Memory, Associative Processing, Parallel Algorithms, and Machine Learning.
This presentation details a fully programmable, associative, content-based, compute in-memory architecture that changes the concept of computing from serial data processing--where data is moved back and forth between the processor and memory--to massive parallel data processing, compute, and search directly in-place.
This associative processing unit (APU) can be used in many machine learning applications, one-shot/few-shot learning, convolutional neural networks, recommender systems and data mining tasks such as prediction, classification, and clustering.
Additionally, the architecture is well-suited to processing large corpora and can be applied to Question Answering (QA) and various NLP tasks such as language translation. The architecture can embed long documents and compute in-place any type of memory network and answer complex questions in O(1).
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About the speaker:
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Dr. Avidan Akeribs is VP of GSI Technology's Associative Computing
Business Unit. He has over 30 years of experience in parallel computing
and In-Place Associative Computing. He has over 25 Granted Patents related
to parallel and in-memory associative computing. Dr. Akeribs has a PhD in
Applied mathematics & Computer Science from the Weismann Instiitute
of Science, Israel.
His specialties are Computational Memory, Associative Processing, Parallel Algorithms, and Machine Learning. |
Contact information:
Avidan Akeribs
GSI Technology