32 citations · 62 across the 5 of their papers we have counts for
Showing cs.ARShow all
3 papers · 1 filter
cs.AR2024
Pack my weights and run! Minimizing overheads for in-memory computing accelerators
Pouya Houshmand, Marian Verhelst
In-memory computing hardware accelerators allow more than 10x improvements in peak efficiency and performance for matrix-vector multiplications (MVM) compared to conventional digit…
cs.AR2023★ 6 cited
Benchmarking and modeling of analog and digital SRAM in-memory computing architectures
Pouya Houshmand, Jiacong Sun, Marian Verhelst
In-memory-computing is emerging as an efficient hardware paradigm for deep neural network accelerators at the edge, enabling to break the memory wall and exploit massive computatio…
cs.AR2022★ 10 cited
Stream: Design Space Exploration of Layer-Fused DNNs on Heterogeneous Dataflow Accelerators
Arne Symons, Linyan Mei, Steven Colleman +3
As the landscape of deep neural networks evolves, heterogeneous dataflow accelerators, in the form of multi-core architectures or chiplet-based designs, promise more flexibility an…