1.9k citations · 2.3k across the 15 of their papers we have counts for
5 papers · 1 filter
The Inherent Adversarial Robustness of Analog In-Memory Computing
Corey Lammie, Julian Büchel, Athanasios Vasilopoulos +2
A key challenge for Deep Neural Network (DNN) algorithms is their vulnerability to adversarial attacks. Inherently non-deterministic compute substrates, such as those based on Anal…
Kernel Approximation using Analog In-Memory Computing
Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos +4
Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approx…
Efficient transformer adaptation for analog in-memory computing via low-rank adapters
Chen Li, Elena Ferro, Corey Lammie +3
Analog In-Memory Computing (AIMC) offers a promising solution to the von Neumann bottleneck. However, deploying transformer models on AIMC remains challenging due to their inherent…
A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing
Elena Ferro, Athanasios Vasilopoulos, Corey Lammie +4
Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is requ…
Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training
Corey Lammie, Athanasios Vasilopoulos, Julian Büchel +4
Analog-Based In-Memory Computing (AIMC) inference accelerators can be used to efficiently execute Deep Neural Network (DNN) inference workloads. However, to mitigate accuracy losse…