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Hossein Golestani

Publications and source records attributed to Hossein Golestani.

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Calipers: A Criticality-aware Framework for Modeling Processor Performance

Computer architecture design space is vast and complex. Tools are needed to explore new ideas and gain insights quickly, with low efforts and at a desired accuracy. We propose Calipers, a criticality-based framework to model key abstractions of complex architectures and a program's execution using dynamic event-dependence graphs. By applying graph algorithms, Calipers can track instruction and event dependencies, compute critical paths, and analyze architecture bottlenecks. By manipulating the graph, Calipers enables architects to investigate a wide range of Instruction Set Architecture (ISA) and microarchitecture design choices/"what-if" scenarios during both early- and late-stage design space exploration without recompiling and rerunning the program. Calipers can model in-order and out-of-order microarchitectures, structural hazards, and different types of ISAs, and can evaluate multiple ideas in a single run. Modeling algorithms are described in detail. We apply Calipers to explore and gain insights in complex microarchitectural and ISA ideas for RISC and EDGE processors, at lower effort than cycle-accurate simulators and with comparable accuracy. For example, among a variety of investigations presented in the paper, experiments show that targeting only a fraction of critical loads can help realize most benefits of value prediction.

cs.PF

CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android

Many mobile applications running on smartphones and wearable devices would potentially benefit from the accuracy and scalability of deep CNN-based machine learning algorithms. However, performance and energy consumption limitations make the execution of such computationally intensive algorithms on mobile devices prohibitive. We present a GPU-accelerated library, dubbed CNNdroid, for execution of trained deep CNNs on Android-based mobile devices. Empirical evaluations show that CNNdroid achieves up to 60X speedup and 130X energy saving on current mobile devices. The CNNdroid open source library is available for download at https://github.com/ENCP/CNNdroid

cs.DC