Julian Büchel

Overview

Title

Pre-Doc

Location

IBM Research Europe - Zurich Zurich, Switzerland

Bio

Julian Büchel is a Pre-Doc in the in-memory computing group at IBM Research Zurich. Büchel studied Computer Science (BSc) and Neural Systems and Computation (MSc) at ETH Zürich. During his MSc, Büchel worked at SynSense, a company focused on digital neuromorphic vision. Büchel joined IBM Research for his MSc thesis in 2021 and is a Pre-Doc since 2022. He is currently doing research in algorithms for analog in-memory computing, hardware-aware training methodologies, adversarial robustness/attacks, and generalization methods in deep learning.

Google Scholar

Publications

AIHWKIT-Lightning: A Scalable HW-Aware Training Toolkit for Analog In-Memory Computing
- - Julian Büchel
  - William Simon
  - et al.
- 2024
- NeurIPS 2024
Analog AI as a Service: A Cloud Platform for In-Memory Computing
- - Kaoutar El Maghraoui
  - Kim Tran
  - et al.
- 2024
- SSE 2024
Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training
- - Corey Liam Lammie
  - A. Vasilopoulos
  - et al.
- 2024
- ISCAS 2024
Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training
- - Corey Liam Lammie
  - A. Vasilopoulos
  - et al.
- 2024
- ISCAS 2024
Design of Analog-AI Hardware Accelerators for Transformer-based Language Models (Invited)
- - Geoffrey Burr
  - Sidney Tsai
  - et al.
- 2023
- IEDM 2023
In-Memory Compute Chips with Carbon-based Projected Phase-Change Memory Devices
- - G.S. Syed
  - K. Brew
  - et al.
- 2023
- IEDM 2023
Programming Weights to Analog In-Memory Computing Cores by Direct Minimization of the Matrix-Vector Multiplication Error
- - Julian Buchel
  - Athanasios Vasilopoulos
  - et al.
- 2023
- IEEE JESTCS
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
- - Manuel Le Gallo
  - Corey Liam Lammie
  - et al.
- 2023
- APL Mach. Learn.
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
  - et al.
- 2023
- Nature Electronics
Exploiting the State Dependency of Conductance Variations in Memristive Devices for Accurate In-Memory Computing
- - Athanasios Vasilopoulos
  - Julian Buchel
  - et al.
- 2023
- IEEE T-ED

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Projects

In-memory computing

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Top collaborators

Julian Büchel

Overview

Title

Location

Bio

Publications

AIHWKIT-Lightning: A Scalable HW-Aware Training Toolkit for Analog In-Memory Computing

Analog AI as a Service: A Cloud Platform for In-Memory Computing

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

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

Design of Analog-AI Hardware Accelerators for Transformer-based Language Models (Invited)

In-Memory Compute Chips with Carbon-based Projected Phase-Change Memory Devices

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

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

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

Exploiting the State Dependency of Conductance Variations in Memristive Devices for Accurate In-Memory Computing

Projects

In-memory computing

Top collaborators

Abu Sebastian

Manuel Le Gallo-Bourdeau

Vijay Narayanan

Athanasios Vasilopoulos