32 citations · 62 across the 5 of their papers we have counts for
5 papers
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…
Analog or Digital In-memory Computing? Benchmarking through Quantitative Modeling
Jiacong Sun, Pouya Houshmand, Marian Verhelst
In-Memory Computing (IMC) has emerged as a promising paradigm for energy-efficient, throughput-efficient and area-efficient machine learning at the edge. However, the differences i…
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…
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…
ZigZag: A Memory-Centric Rapid DNN Accelerator Design Space Exploration Framework
Linyan Mei, Pouya Houshmand, Vikram Jain +2
Building efficient embedded deep learning systems requires a tight co-design between DNN algorithms, memory hierarchy, and dataflow. However, owing to the large degrees of freedom…