Please register here to attend the EE Seminars via Zoom this spring. Once you register, you will receive the Zoom link. You only need to register once to be able to attend any of the seminars. 

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We witnessed a 300,000 times increase in the amount of compute for AI in the past decade. The latest natural language processing model is fueled with over trillion parameters while the memory need of neural recommendation and ranking models has grown from hundreds of gigabyte to the terabyte scale. This talk will introduce the underinvested deep learning personalization and recommendation systems in the overall system research community. The training of state-of-the-art industry-scale personalization and recommendation models consumes the highest number of compute cycles among all deep learning use cases at Facebook. For AI inference, recommendation use cases consume even higher compute cycles of 80%. What are the key system challenges faced by industry-scale neural personalization and recommendation models? This talk will highlight recent advances on AI system development for deep learning recommendation and the implications on infrastructure optimization opportunities across the machine learning system stack. System research for deep learning recommendation and AI at large is at a nascent stage. This talk will conclude with research directions for building and designing responsible AI systems – that is fair, efficient, and environmentally sustainable.