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Code generation for embedded heterogeneous architectures on android

https://doi.org/10.7873/date2014.099

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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/cpe.4286openalex-snapshot

Accelerating Linux and Android applications on low‐power devices through remote GPGPU offloading

inferred← citeshttps://doi.org/10.1007/978-3-319-26408-0_12openalex-snapshot

HIPAcc

inferred← citeshttps://doi.org/10.1007/978-3-319-26408-0_13openalex-snapshot

ReconOS

inferred← citeshttps://doi.org/10.1007/978-3-319-26408-0_14openalex-snapshot

The LEAP FPGA Operating System

inferred← citeshttps://doi.org/10.1007/978-3-319-26408-0_2openalex-snapshot

High-Level Synthesis

inferred← citeshttps://doi.org/10.1007/978-3-319-26408-0_3openalex-snapshot

A Quick Tour of High-Level Synthesis Solutions for FPGAs

inferred← citeshttps://doi.org/10.1007/978-3-319-26408-0_7openalex-snapshot

Big Data and HPC Acceleration with Vivado HLS

inferred← citeshttps://doi.org/10.1109/iccad.2017.8203894openalex-snapshot

Generating FPGA-based image processing accelerators with Hipacc: (Invited paper)

inferred← citeshttps://doi.org/10.1109/snpd.2015.7176197openalex-snapshot

Portable parallelized blowfish via RenderScript

inferred← citeshttps://doi.org/10.1109/tpds.2015.2394802openalex-snapshot

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

inferred← citeshttps://doi.org/10.1145/2968455.2968521openalex-snapshot

Matrix multiplication beyond auto-tuning

inferred← citeshttps://doi.org/10.13053/rcs-78-1-12openalex-snapshot

Parallelization Strategy Based on RenderScript Reductions

inferred← citeshttps://doi.org/10.5430/jbei.v1n1p59openalex-snapshot

Lessons learned from the development of an affordable fall detection system for mHealth

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