Compiler-driven error analysis for designing approximate accelerators. - Jorge Castro-Godínez; Sven Esser; Muhammad Shafique 001; Santiago Pagani; Jörg Henkel (DATE, 2018)

ECAx: Balancing Error Correction Costs in Approximate Accelerators. - Jorge Castro-Godínez; Muhammad Shafique 001; Jörg Henkel (ACM Trans. Embed. Comput. Syst., 2019)

Towards Quality-Driven Approximate Software Generation for Accurate Hardware: Work-in-Progress. - Jorge Castro-Godínez; Muhammad Shafique 001; Jörg Henkel (CASES, 2020)

AxHLS: Design Space Exploration and High-Level Synthesis of Approximate Accelerators using Approximate Functional Units and Analytical Models. - Jorge Castro-Godínez; Julián Mateus-Vargas; Muhammad Shafique 001; Jörg Henkel (ICCAD, 2020)

Approximate Acceleration for CNN-based Applications on IoT Edge Devices. - Jorge Castro-Godínez; Deykel Hernández-Araya; Muhammad Shafique 001; Jörg Henkel (LASCAS, 2020)

AUGER: A Tool for Generating Approximate Arithmetic Circuits. - Deykel Hernández-Araya; Jorge Castro-Godínez; Muhammad Shafique 001; Jörg Henkel (LASCAS, 2020)

Multiple approximate instances in neural processing units for energy-efficient circuit synthesis: work-in-progress. - Tanfer Alan; Jorge Castro-Godínez; Jörg Henkel (CASES, 2021)

TailoredCore: Generating Application-Specific RISC-V-based Cores. - Jeferson González-Gómez; Steven Ávila-Ardón; Jonathan Rojas-González; Andres Stephen-Cantillano; Jorge Castro-Godínez; Carlos Salazar-García; Muhammad Shafique 001; Jörg Henkel (LASCAS, 2021)

AxLS: A Framework for Approximate Logic Synthesis Based on Netlist Transformations. - Jorge Castro-Godínez; Humberto Barrantes-García; Muhammad Shafique 001; Jörg Henkel (IEEE Trans. Circuits Syst. II Express Briefs, 2021)

Automatic Floorplanning and Standalone Generation of Bitstream-Level IP Cores. - Nadir Khan; Jorge Castro-Godínez; Shixiang Xue; Jörg Henkel; Jürgen Becker 001 (IEEE Trans. Very Large Scale Integr. Syst., 2021)

Automated Design of Approximate Accelerators. - Jorge Castro-Godínez (2021)

AxRSU: Approximate Radix-4 Squarer Unit. - Morgana Macedo Azevedo da Rosa; Guilherme Paim; Jorge Castro-Godínez; Eduardo A. C. da Costa; Rafael Iankowski Soares; Sergio Bampi (ISCAS, 2022)

On the Netlist Gate-level Pruning for Tree-based Machine Learning Accelerators. - Brunno A. Abreu; Guilherme Paim; Jorge Castro-Godínez; Mateus Grellert; Sergio Bampi (LASCAS, 2022)

Approximating HW Accelerators through Partial Extractions onto Shared Artificial Neural Networks. - Prattay Chowdhury; Jorge Castro-Godínez; Benjamin Carrion Schafer (ASP-DAC, 2023)

Cross-Layer Approximations for System-Level Optimizations: Challenges and Opportunities. - Jorge Castro-Godínez; Muhammad Abdullah Hanif; Muhammad Shafique 001 (DSN-W, 2023)

Generic Accuracy Configurable Matrix Multiplication-Addition Accelerator using HLS. - Luis G. León-Vega; Jorge Castro-Godínez (DSN-W, 2023)

Automatic Generation of Resource and Accuracy Configurable Processing Elements. - Luis G. León-Vega; Eduardo Salazar-Villalobos; Alejandro Rodriguez-Figueroa; Jorge Castro-Godínez (ACM Trans. Embed. Comput. Syst., 2023)

A User-Friendly Ecosystem for AI FPGA-Based Accelerators. - Luis G. León-Vega; Erick Obregon-Fonseca; Jorge Castro-Godínez (COINS, 2024)

Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers. - Mohammad Javad Askarizadeh; Ebrahim Farahmand; Jorge Castro-Godínez; Ali Mahani 001; Laura Cabrera Quiros; Carlos Salazar-Garcia (CoRR, 2024)

Improving Netlist Transformation-Based Approximate Logic Synthesis Through Resynthesis. - Roger Morales-Monge; Jorge Castro-Godínez; Guilherme Paim (IEEE Embed. Syst. Lett., 2024)

Power-efficient Approximate Multipliers for Classification Tasks in Neural Networks. - Vínicius Zanandrea; Jorge Castro-Godínez; Cristina Meinhardt (LASCAS, 2025)

Ultra-Compact Approximate 4:2 Compressor on the Design of Power-Efficient Multipliers for Image Multiplication. - Vínicius Zanandrea; Jorge Castro-Godínez; Cristina Meinhardt (LASCAS, 2025)

A Quantitative Evaluation of Approximate Softmax Functions for Deep Neural Networks. - Fabricio Elizondo-Fernández; Luis G. León-Vega; Cristina Meinhardt; Jorge Castro-Godínez (CoRR, 2025)