In this paper, we introduce Optimus: an optimizing synthesis compiler for streaming applications. Optimus compiles programs written in a high level streaming language to either so...
Amir Hormati, Manjunath Kudlur, Scott A. Mahlke, D...
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
In this work we try to bridge the gap often encountered by researchers who find themselves with few or no labeled examples from their desired target domain, yet still have access ...
Digital Signal Processing (DSP) is becoming increasingly widespread in portable devices. Due to harsh constraints on power, latency, and throughput in embedded environments, devel...
Sitij Agrawal, William Thies, Saman P. Amarasinghe
In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...