We study Euclidean embeddings of Euclidean metrics and present the following four results: (1) an O(log3 n √ log log n) approximation for minimum bandwidth in conjunction with a ...
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...
Scratchpad memory has been introduced as a replacement for cache memory as it improves the performance of certain embedded systems. Additionally, it has also been demonstrated tha...
Andhi Janapsatya, Aleksandar Ignjatovic, Sri Param...
Embedded Architect is a design automation tool that embodies a static performance evaluation technique to support early, architecture-level design space exploration for component-...
: Hard metrics are the class of extremal metrics with respect to embedding into Euclidean spaces: they incur Ω(logn) multiplicative distortion, which is as large as it can possib...