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• W2530616559 abstract "Local memory is a key factor for the performance of accelerators in SoCs. Despite technology scaling, the gap between on-chip storage and memory footprint of embedded applications keeps widening. We present a solution to preserve the speedup of accelerators when scaling from small to large data sets. Combining specialized DMA and address translation with a software layer in Linux, our design is transparent to user applications and broadly applicable to any class of SoCs hosting high-throughput accelerators. We demonstrate the robustness of our design across many heterogeneous workload scenarios and memory allocation policies with FPGA-based SoC prototypes featuring twelve concurrent accelerators accessing up to 768MB out of 1GB-addressable DRAM." @default.
• W2530616559 created "2016-10-21" @default.
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• W2530616559 date "2016-10-01" @default.
• W2530616559 modified "2023-10-18" @default.
• W2530616559 title "Handling large data sets for high-performance embedded applications in heterogeneous systems-on-chip" @default.
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• W2530616559 doi "https://doi.org/10.1145/2968455.2968509" @default.
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