Dr. Avidan Akerib is the 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. Akerib 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.
Conventional CNNs require a finite number of pre-known classes with many examples. In this webinar, we present an associative computing approach and chip architecture for Few Shot training networks using O (1) similarity search and Top K computing. This approach enables only few examples per label, extreme classification and fast adjustment.