S
Soheil Ghiasi
Researcher at University of California, Davis
Publications - 107
Citations - 2152
Soheil Ghiasi is an academic researcher from University of California, Davis. The author has contributed to research in topics: Control reconfiguration & Throughput (business). The author has an hindex of 17, co-authored 96 publications receiving 1881 citations. Previous affiliations of Soheil Ghiasi include University of California & University of California, Los Angeles.
Papers
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Journal ArticleDOI
Optimal Energy Aware Clustering in Sensor Networks
TL;DR: The theoretical aspects of the clustering problem in sensor networks with application to energy optimization are studied and an optimal algorithm for clustering the sensor nodes such that each cluster is balanced and the total distance between sensor nodes and master nodes is minimized is illustrated.
Posted Content
Hardware-Oriented Approximation of Convolutional Neural Networks
TL;DR: Ristretto is a model approximation framework that analyzes a given CNN with respect to numerical resolution used in representing weights and outputs of convolutional and fully connected layers and can condense models by using fixed point arithmetic and representation instead of floating point.
Journal ArticleDOI
Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks
TL;DR: Ristretto is a CNN approximation framework that enables empirical investigation of the tradeoff between various number representation and word width choices and the classification accuracy of the model and is used to demonstrate that three ImageNet networks can be condensed to use 8-bit dynamic fixed point for network weights and activations.
Proceedings ArticleDOI
Design space exploration of FPGA-based Deep Convolutional Neural Networks
TL;DR: This paper proposes an FPGA-based accelerator architecture which leverages all sources of parallelism in DCNNs, and develops analytical feasibility and performance estimation models that take into account various design and platform parameters.
Proceedings ArticleDOI
CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android
TL;DR: CNNdroid as mentioned in this paper is a GPU-accelerated library for execution of trained deep CNNs on Android-based mobile devices, which achieves up to 60X speedup and 130X energy saving on current mobile devices.