EE380 Computer Systems Colloquium: Enabling NLP, Machine Learning, and Few-Shot Learning using Associative Processing

Topic: 
Enabling NLP, Machine Learning, and Few-Shot Learning using Associative Processing
Wednesday, November 8, 2017 - 4:30pm
Venue: 
Gates B03
Speaker: 
Avidan Akerib (VP of Associative Computing Business Unit GSI Technologies)
Abstract / Description: 

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).

Bio:

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.