1 citations · 1 across the 4 of their papers we have counts for
4 papers
When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning
Roberto Morabito, Guanghan Wu
Large Language Models (LLMs) are increasingly used to automate software generation in embedded machine learning workflows, yet their outputs often fail silently or behave unpredict…
Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques
Adarsh Prasad Behera, Jaya Prakash Champati, Roberto Morabito +2
Recent progress in Language Models (LMs) has dramatically advanced the field of natural language processing (NLP), excelling at tasks like text generation, summarization, and quest…
Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical
Adarsh Prasad Behera, Paulius Daubaris, Iñaki Bravo +4
On-device inference holds great potential for increased energy efficiency, responsiveness, and privacy in edge ML systems. However, due to less capable ML models that can be embedd…
Improved Decision Module Selection for Hierarchical Inference in Resource-Constrained Edge Devices
Adarsh Prasad Behera, Roberto Morabito, Joerg Widmer +1
The Hierarchical Inference (HI) paradigm employs a tiered processing: the inference from simple data samples are accepted at the end device, while complex data samples are offloade…