4 papers
SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures
Leandro Fiorin, Marco Ronzani, Cristina Silvano
Mixed-precision computation has been introduced in deep neural networks (DNNs) as an effective approach to reduce latency, energy consumption, and memory footprint. However, effici…
Hypergraph Partitioning on GPU with Distinct Incident Hyperedges and Size Constraints
Marco Ronzani, Cristina Silvano
Hypergraph partitioning is a recurring NP-hard problem in engineering; its efficient solution at scale hinges on parallelism. This work proposes a GPU-centric algorithm for multi-l…
A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware
Marco Ronzani, Cristina Silvano
Executing Spiking Neural Networks (SNNs) on neuromorphic hardware poses the problem of mapping neurons to cores. SNNs operate by propagating spikes between neurons that form a grap…
Incidence Constraints in Hypergraph Partitioning on GPU
Marco Ronzani, Cristina Silvano
Hypergraph partitioning is a pervasive NP-hard problem, and accelerating its computation on GPU can both slice time-to-solution and raise quality of results. In this work, we imple…