30 citations · 56 across the 7 of their papers we have counts for
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cs.LG2022★ 3 cited
XEngine: Optimal Tensor Rematerialization for Neural Networks in Heterogeneous Environments
Manuela Schuler, Richard Membarth, Philipp Slusallek
Memory efficiency is crucial in training deep learning networks on resource-restricted devices. During backpropagation, forward tensors are used to calculate gradients. Despite the…
cs.DC2022★ 14 cited
AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs
André Müller, Bertil Schmidt, Richard Membarth +2
In recent years, the rapidly increasing number of reads produced by next-generation sequencing (NGS) technologies has driven the demand for efficient implementations of sequence al…