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

18 July 2023

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

7 / 7 papers shown

Title
NeuroSim V1.5: Improved Software Backbone for Benchmarking Compute-in-Memory Accelerators with Device and Circuit-level Non-idealities James Read Ming-Yen Lee Wei-Hsing Huang Yuan-Chun Luo A. Lu Shimeng Yu 44 0 0 05 May 2025
The Inherent Adversarial Robustness of Analog In-Memory Computing Corey Lammie Julian Büchel A. Vasilopoulos Manuel Le Gallo Abu Sebastian AAML 54 0 0 11 Nov 2024
Kernel Approximation using Analog In-Memory Computing Julian Büchel Giacomo Camposampiero A. Vasilopoulos Corey Lammie Manuel Le Gallo Abbas Rahimi Abu Sebastian 58 0 0 05 Nov 2024
In-Materia Speech Recognition Mohamadreza Zolfagharinejad Julian Büchel Lorenzo Cassola Sachin Kinge Ghazi Sarwat Syed Abu Sebastian Wilfred G. van der Wiel 31 0 0 14 Oct 2024
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing Jens Egholm Pedersen Steven Abreu Matthias Jobst Gregor Lenz Vittorio Fra ... Gianvito Urgese Sadasivan Shankar Terrence C. Stewart Jason K. Eshraghian Sadique Sheik 34 30 0 24 Nov 2023
Fast offset corrected in-memory training Malte J. Rasch F. Carta Omobayode Fagbohungbe Tayfun Gokmen 39 6 0 08 Mar 2023
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators Malte J. Rasch C. Mackin Manuel Le Gallo An Chen A. Fasoli ... P. Narayanan H. Tsai G. Burr Abu Sebastian Vijay Narayanan 18 86 0 16 Feb 2023