47 citations · 141 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
DeFiNES: Enabling Fast Exploration of the Depth-first Scheduling Space for DNN Accelerators through Analytical Modeling
Linyan Mei, Koen Goetschalckx, Arne Symons +1
DNN workloads can be scheduled onto DNN accelerators in many different ways: from layer-by-layer scheduling to cross-layer depth-first scheduling (a.k.a. layer fusion, or cascaded…
DPU-v2: Energy-efficient execution of irregular directed acyclic graphs
Nimish Shah, Wannes Meert, Marian Verhelst
A growing number of applications like probabilistic machine learning, sparse linear algebra, robotic navigation, etc., exhibit irregular data flow computation that can be modeled w…
Hardware-aware mobile building block evaluation for computer vision
Maxim Bonnaerens, Matthias Freiberger, Marian Verhelst +1
In this work we propose a methodology to accurately evaluate and compare the performance of efficient neural network building blocks for computer vision in a hardware-aware manner.…
Delta Keyword Transformer: Bringing Transformers to the Edge through Dynamically Pruned Multi-Head Self-Attention
Zuzana Jelčicová, Marian Verhelst
Multi-head self-attention forms the core of Transformer networks. However, their quadratically growing complexity with respect to the input sequence length impedes their deployment…