200 citations · 513 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
SALSA: Simulated Annealing based Loop-Ordering Scheduler for DNN Accelerators
Victor J. B. Jung, Arne Symons, Linyan Mei +2
To meet the growing need for computational power for DNNs, multiple specialized hardware architectures have been proposed. Each DNN layer should be mapped onto the hardware with th…
Real-Time Acoustic Perception for Automotive Applications
Jun Yin, Stefano Damiano, Marian Verhelst +2
In recent years the automotive industry has been strongly promoting the development of smart cars, equipped with multi-modal sensors to gather information about the surroundings, i…
TinyVers: A Tiny Versatile System-on-chip with State-Retentive eMRAM for ML Inference at the Extreme Edge
Vikram Jain, Sebastian Giraldo, Jaro De Roose +3
Extreme edge devices or Internet-of-thing nodes require both ultra-low power always-on processing as well as the ability to do on-demand sampling and processing. Moreover, support…