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To characterize tensors, determining obstructions to tensor restriction and degeneration is a fundamental problem. In this talk, I will introduce a geometric invariant of tensors, called the multilinear Fano scheme, and we use it to provide some...
Youssef is an incoming postdoctoral scholar at Stanford University. Youssef is interested in the principles of trustworthy machine learning. He has worked on privacy, robustness, and unlearning, including in distributed settings.
No abstract available.
The computation demands of 21st-century abundant-data workloads, such as AI/Machine Learning, far exceed the capabilities of today’s computing systems. For example, a Dream Chip would ideally co-locate all memory and compute on a single chip, quickly accessible at low energy. Computing systems instead use large off-chip memory and spend enormous time and energy shuttling data back and forth. This memory wall gets worse as conventional 2D miniaturization gets increasingly difficult.
The next leap in computing requires transformative NanoSystems. We create new chip architectures through ultra-dense 3D integration of logic and memory – the N3XT 3D approach. Multiple N3XT 3D chips are integrated through a continuum of chip stacking/interposer/wafer-level integration — the N3XT 3D MOSAIC. To scale with growing problem sizes, new Illusion systems orchestrate workload execution on N3XT 3D MOSAIC creating an illusion of a Dream Chip with near-Dream energy and throughput.
Several hardware prototypes, built in industrial facilities resulting from lab-to-fab activities, demonstrate the effectiveness of our approach. We target 1,000X system-level Energy-Delay-Product benefits, especially for abundant-data workloads. We also address new ways of ensuring robust system operation despite growing challenges of design bugs, manufacturing defects, reliability failures, and security attacks.
