1.9k citations · 2.3k across the 15 of their papers we have counts for
16 papers
Modern analog computing for solving differential and matrix equations
Zhong Sun, Piergiulio Mannocci, Manuel Le Gallo +1
In recent years, driven by the computational demands of data-intensive applications such as artificial intelligence and scientific computing, analog computing has gained renewed in…
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…
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
Hadjer Benmeziane, Corey Lammie, Irem Boybat +9
The advancement of Deep Learning (DL) is driven by efficient Deep Neural Network (DNN) design and new hardware accelerators. Current DNN design is primarily tailored for general-pu…
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Malte J. Rasch, Charles Mackin, Manuel Le Gallo +10
Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only app…
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator
Chuteng Zhou, Fernando Garcia Redondo, Julian Büchel +7
Always-on TinyML perception tasks in IoT applications require very high energy efficiency. Analog compute-in-memory (CiM) using non-volatile memory (NVM) promises high efficiency a…
A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays
Malte J. Rasch, Diego Moreda, Tayfun Gokmen +6
We introduce the IBM Analog Hardware Acceleration Kit, a new and first of a kind open source toolkit to simulate analog crossbar arrays in a convenient fashion from within PyTorch…
Robust High-dimensional Memory-augmented Neural Networks
Geethan Karunaratne, Manuel Schmuck, Manuel Le Gallo +4
Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapti…
Parallel convolution processing using an integrated photonic tensor core
Johannes Feldmann, Nathan Youngblood, Maxim Karpov +13
With the proliferation of ultra-high-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence, the world is generating exponentially inc…
Mixed-precision deep learning based on computational memory
S. R. Nandakumar, Manuel Le Gallo, Christophe Piveteau +11
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition…
In-memory hyperdimensional computing
Geethan Karunaratne, Manuel Le Gallo, Giovanni Cherubini +3
Hyperdimensional computing (HDC) is an emerging computational framework that takes inspiration from attributes of neuronal circuits such as hyperdimensionality, fully distributed h…
In-memory computing on a photonic platform
Carlos Ríos, Nathan Youngblood, Zengguang Cheng +5
Collocated data processing and storage are the norm in biological systems. Indeed, the von Neumann computing architecture, that physically and temporally separates processing and m…
Fatiguing STDP: Learning from Spike-Timing Codes in the Presence of Rate Codes
Timoleon Moraitis, Abu Sebastian, Irem Boybat +3
Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of e…