21 citations · 26 across the 6 of their papers we have counts for
8 papers
Exact Nearest-Neighbor Search on Energy-Efficient FPGA Devices
Patrizio Dazzi, William Guglielmo, Franco Maria Nardini +2
This paper investigates the usage of FPGA devices for energy-efficient exact kNN search in high-dimension latent spaces. This work intercepts a relevant trend that tries to support…
Blending Learning to Rank and Dense Representations for Efficient and Effective Cascades
Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto +1
We investigate the exploitation of both lexical and neural relevance signals for ad-hoc passage retrieval. Our exploration involves a large-scale training dataset in which dense ne…
Power- and Fragmentation-aware Online Scheduling for GPU Datacenters
Francesco Lettich, Emanuele Carlini, Franco Maria Nardini +2
The rise of Artificial Intelligence and Large Language Models is driving increased GPU usage in data centers for complex training and inference tasks, impacting operational costs,…
Early Exit Strategies for Approximate k-NN Search in Dense Retrieval
Francesco Busolin, Claudio Lucchese, Franco Maria Nardini +3
Learned dense representations are a popular family of techniques for encoding queries and documents using high-dimensional embeddings, which enable retrieval by performing approxim…
Neural Network Compression using Binarization and Few Full-Precision Weights
Franco Maria Nardini, Cosimo Rulli, Salvatore Trani +1
Quantization and pruning are two effective Deep Neural Networks model compression methods. In this paper, we propose Automatic Prune Binarization (APB), a novel compression techniq…
Distilled Neural Networks for Efficient Learning to Rank
F. M. Nardini, C. Rulli, S. Trani +1
Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to bec…
Learning Early Exit Strategies for Additive Ranking Ensembles
Francesco Busolin, Claudio Lucchese, Franco Maria Nardini +3
Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees. We propose LEAR, a novel - learned - technique aimed to reduce the…
Query-level Early Exit for Additive Learning-to-Rank Ensembles
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando +2
Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. The tight constraints on query response time recently motivated researchers…