Julian Büchel

According to our database, Julian Büchel authored at least 13 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training.

Corey Lammie, Athanasios Vasilopoulos, Julian Büchel, Giacomo Camposampiero, Manuel Le Gallo, Malte J. Rasch, Abu Sebastian

CoRR, 2024

2023

Programming Weights to Analog In-Memory Computing Cores by Direct Minimization of the Matrix-Vector Multiplication Error.

Julian Büchel, Athanasios Vasilopoulos, Benedikt Kersting, Corey Lammie, Kevin Brew, Timothy Philip, Nicole Saulnier, Vijay Narayanan, Manuel Le Gallo, Abu Sebastian

IEEE J. Emerg. Sel. Topics Circuits Syst., December, 2023

Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference.

Manuel Le Gallo, Corey Lammie, Julian Büchel, Fabio Carta, Omobayode Fagbohungbe, Charles Mackin, Hsinyu Tsai, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui, Malte J. Rasch

CoRR, 2023

Gradient descent-based programming of analog in-memory computing cores.

Julian Büchel, Athanasios Vasilopoulos, Benedikt Kersting, Frédéric Odermatt, Kevin Brew, Injo Ok, Samuel Choi, Iqbal Saraf, Victor Chan, Timothy Philip, Nicole Saulnier, Vijay Narayanan, Manuel Le Gallo, Abu Sebastian

CoRR, 2023

2022

ML-HW Co-Design of Noise-Robust TinyML Models and Always-On Analog Compute-in-Memory Edge Accelerator.

Chuteng Zhou, Fernando García-Redondo, Julian Büchel, Irem Boybat, Xavier Timoneda Comas, S. R. Nandakumar, Shidhartha Das, Abu Sebastian, Manuel Le Gallo, Paul N. Whatmough

IEEE Micro, 2022

A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference.

Manuel Le Gallo, Riduan Khaddam-Aljameh, Milos Stanisavljevic, Athanasios Vasilopoulos, Benedikt Kersting, Martino Dazzi, Geethan Karunaratne, Matthias Braendli, Abhairaj Singh, Silvia M. Mueller, Julian Büchel, Xavier Timoneda Comas, Vinay Joshi, Urs Egger, Angelo Garofalo, Anastasios Petropoulos, Theodore Antonakopoulos, Kevin Brew, Samuel Choi, Injo Ok, Timothy Philip, Victor Chan, Mary Claire Silvestre, Ishtiaq Ahsan, Nicole Saulnier, Vijay Narayanan, Pier Andrea Francese, Evangelos Eleftheriou, Abu Sebastian

CoRR, 2022

Network Insensitivity to Parameter Noise via Parameter Attack During Training.

Julian Büchel, Fynn Firouz Faber, Dylan Richard Muir

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator.

Chuteng Zhou, Fernando García-Redondo, Julian Büchel, Irem Boybat, Xavier Timoneda Comas, S. R. Nandakumar, Shidhartha Das, Abu Sebastian, Manuel Le Gallo, Paul N. Whatmough

CoRR, 2021

Adversarial Attacks on Spiking Convolutional Networks for Event-based Vision.

Julian Büchel, Gregor Lenz, Yalun Hu, Sadique Sheik, Martino Sorbaro

CoRR, 2021

Network insensitivity to parameter noise via adversarial regularization.

Julian Büchel, Fynn Faber, Dylan R. Muir

CoRR, 2021

Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors.

Julian Büchel, Dmitrii Zendrikov, Sergio M. G. Solinas, Giacomo Indiveri, Dylan R. Muir

CoRR, 2021

Implementing Efficient Balanced Networks with Mixed-Signal Spike-Based Learning Circuits.

Julian Büchel, Jonathan Kakon, Michel Perez, Giacomo Indiveri

Proceedings of the IEEE International Symposium on Circuits and Systems, 2021

2018

Ladder Networks for Semi-Supervised Hyperspectral Image Classification.

Julian Büchel, Okan K. Ersoy

CoRR, 2018