200 citations · 513 across the 24 of their papers we have counts for
29 papers
CNN-based Robust Sound Source Localization with SRP-PHAT for the Extreme Edge
Jun Yin, Marian Verhelst
Robust sound source localization for environments with noise and reverberation are increasingly exploiting deep neural networks fed with various acoustic features. Yet, state-of-th…
COAC: Cross-layer Optimization of Accelerator Configurability for Efficient CNN Processing
Steven Colleman, Man Shi, Marian Verhelst
To achieve high accuracy, convolutional neural networks (CNNs) are increasingly growing in complexity and diversity in layer types and topologies. This makes it very challenging to…
CMDS: Cross-layer Dataflow Optimization for DNN Accelerators Exploiting Multi-bank Memories
Man Shi, Steven Colleman, Charlotte VanDeMieroop +4
Deep neural networks (DNN) use a wide range of network topologies to achieve high accuracy within diverse applications. This model diversity makes it impossible to identify a singl…
PATRONoC: Parallel AXI Transport Reducing Overhead for Networks-on-Chip targeting Multi-Accelerator DNN Platforms at the Edge
Vikram Jain, Matheus Cavalcante, Nazareno Bruschi +6
Emerging deep neural network (DNN) applications require high-performance multi-core hardware acceleration with large data bursts. Classical network-on-chips (NoCs) use serial packe…
Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference
Matteo Risso, Alessio Burrello, Giuseppe Maria Sarda +5
The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a di…
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…