Published in 2026 Workshop on Languages, Tools, and Techniques for Accelerator Design (LATTE), 2026
This paper presents a vision of an open-source platform for AI accelerator development.
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Published in 2025 International Conference on Computer-Aided Design (ICCAD), 2025
This paper presents the RISC-V Vector Instruction integration with TVM’s Metaschedule for the tuning of tensor operations.
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Published in 2025 IEEE European Test Symposium (ETS), 2025
This paper presents a first approach to systematically ask LLMs to generate functional tests based on the description of a custom hardware accelerator and the interfaces used to program it.
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Published in 27th Euromicro Conference Series on Digital System Design (DSD) 2024, 2024
This paper presents a workflow for deployment of CNNs on custom hardware accelerators, taken into account pruning, quantization, and tuning. The workflow was realized using TVM.
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Published in arXiv, 2024
This paper presents a systematic review of papers distributing the execution of CNNs across multiple devices.
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Published in , 2024
A method to prune in an iterative manner CNNs, taking into account the connectivity graph of layers.
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Published in Design, Automation and Test in Europe Conference, 2024
This paper presents a new method to model the problem of distributing the inference of a NN across multiple devices.
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Published in 2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2023
This paper presents a comparison of execution time of different Neural Networks on different FPGA-based open source AI Accelerators.
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Published in IEEE International Smart Cities Conference (ISC2) 2023, 2023
This paper presents a survey on methods and techniques for traffic surveillance using multiple POV cameras.
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Published in ESWEEK Workshop: CODAI 2023, 2022
This paper explains the integration added to the TVM framework to generate C code to accelerate neural networks on the Gemmini accelerator
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Published in arXiv, 2021
Paper result from the thesis for the title of Electronic Engineer attained in 2021 at the Universidad Tecnologica Nacional (UTN), Argentina.
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