Search Results for author: Mikel Lujan

Found 4 papers, 1 papers with code

Tiny Classifier Circuits: Evolving Accelerators for Tabular Data

no code implementations28 Feb 2023 Konstantinos Iordanou, Timothy Atkinson, Emre Ozer, Jedrzej Kufel, John Biggs, Gavin Brown, Mikel Lujan

This paper proposes a methodology for automatically generating predictor circuits for classification of tabular data with comparable prediction performance to conventional ML techniques while using substantially fewer hardware resources and power.


QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking

no code implementations25 Nov 2021 Xidan Song, Edoardo Manino, Luiz Sena, Erickson Alves, Eddie de Lima Filho, Iury Bessa, Mikel Lujan, Lucas Cordeiro

QNNVerifier is the first open-source tool for verifying implementations of neural networks that takes into account the finite word-length (i. e. quantization) of their operands.


Energy Predictive Models for Convolutional Neural Networks on Mobile Platforms

no code implementations10 Apr 2020 Crefeda Faviola Rodrigues, Graham Riley, Mikel Lujan

To address this issue, we provide a comprehensive analysis of building regression-based predictive models for deep learning on mobile devices, based on empirical measurements gathered from the SyNERGY framework. Our predictive modelling strategy is based on two types of predictive models used in the literature:individual layers and layer-type.

To Ensemble or Not Ensemble: When does End-To-End Training Fail?

1 code implementation12 Feb 2019 Andrew M. Webb, Charles Reynolds, Wenlin Chen, Henry Reeve, Dan-Andrei Iliescu, Mikel Lujan, Gavin Brown

An interesting question is whether this trend will continue-are there any clear failure cases for E2E training?

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