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• W3213528054 abstract "DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments. This makes it difficult to appreciate the impact of Systemon-Chip (SoC) resource contention, OS overheads, and programming-stack inefficiencies on overall performance/energy-efficiency. To address this challenge, we present Gemmini, an open-source, full-stack DNN accelerator generator. Gemmini generates a wide design-space of efficient ASIC accelerators from a flexible architectural template, together with flexible programming stacks and full SoCs with shared resources that capture system-level effects. Gemmini-generated accelerators have also been fabricated, delivering up to three orders-of-magnitude speedups over high-performance CPUs on various DNN benchmarks." @default.
• W3213528054 created "2021-11-22" @default.
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• W3213528054 date "2021-12-05" @default.
• W3213528054 modified "2023-10-16" @default.
• W3213528054 title "Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration" @default.
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• W3213528054 doi "https://doi.org/10.1109/dac18074.2021.9586216" @default.
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