3 papers
cs.AR2026
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…
cs.AR2025
A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures
Serena Curzel, Fabrizio Ferrandi, Leandro Fiorin +15
Given their increasing size and complexity, the need for efficient execution of deep neural networks has become increasingly pressing in the design of heterogeneous High-Performanc…
cs.AR2025
A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Cristina Silvano, Daniele Ielmini, Fabrizio Ferrandi +19
Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vi…