How to Model the Training and Inference of Analog-Based In-Memory Computing (AIMC) Systems
- 2024
- ISCAS 2024
Publications
107 results for Manuel Le Gallo-Bourdeau
Hardware implementation of memristor-based artificial neural networks
- Fernando Aguirre
- Abu Sebastian
- et al.
- 2024
- Nature Communications
Designing Circuits for AiMC Based on Non-Volatile Memories: A Tutorial Brief on Trade-Off and Strategies for ADCs and DACs Co-Design
- R. Vignali
- R. Zurla
- et al.
- 2024
- IEEE TCAS-II
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
- 2023
- IEEE JESTCS
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
- 2023
- APL Mach. Learn.
Deep neural network inference with a 64-core in-memory compute chip based on phase-change memory
- 2023
- E\PCOS 2023
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
- Malte J. Rasch
- Charles Mackin
- et al.
- 2023
- Nature Communications
Deep neural network inference with a 64-core in-memory compute chip based on phase-change memory
- 2023
- NVMTS 2023