Cevher Lab

Research focus

ERC Consolidator Grant · 2016

Time-Data Trade-Offs in Resource-Constrained Information and Inference Systems

Massive data poses a fundamental challenge to learning algorithms, which is captured by the following computational dogma: The running time of an algorithm increases with the size of its input data. The available computational power, however, is growing slowly relative to data sizes. Hence, large-scale problems of interest require increasingly more time to solve. Our recent research demonstrates that this dogma is false in general, and supports an emerging perspective: Data should be treated as a resource that can be traded off with other resources such as running time. For data acquisition and communications, we have also shown related sampling, energy, and circuit area trade-offs. A…

From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.