3 citations · 5 across the 3 of their papers we have counts for
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cs.AR2024
Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs
Endri Taka, Dimitrios Gourounas, Andreas Gerstlauer +2
FPGAs are a promising platform for accelerating Deep Learning (DL) applications, due to their high performance, low power consumption, and reconfigurability. Recently, the leading…
cs.AR2023★ 2 cited
Lightweight ML-based Runtime Prefetcher Selection on Many-core Platforms
Erika S. Alcorta, Mahesh Madhav, Scott Tetrick +2
Modern computer designs support composite prefetching, where multiple individual prefetcher components are used to target different memory access patterns. However, multiple prefet…