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Luca Benini
202 publications
Osvaldo Simeone
158 publications
Davide Rossi
44 publications
Francesco Conti
39 publications
Ning Li
38 publications
Sergi Abadal
31 publications
Eduard Alarcón
25 publications
Denis Kleyko
23 publications
Abbas Rahimi
21 publications
David Atienza
21 publications
Bipin Rajendran
17 publications

research

∙ 07/18/2023

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

Analog In-Memory Computing (AIMC) is a promising approach to reduce the ...

0 Manuel Le Gallo, et al. ∙

research

∙ 05/26/2023

Gradient descent-based programming of analog in-memory computing cores

The precise programming of crossbar arrays of unit-cells is crucial for ...

0 Julian Büchel, et al. ∙

research

∙ 05/17/2023

AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing

The advancement of Deep Learning (DL) is driven by efficient Deep Neural...

1 Hadjer Benmeziane, et al. ∙

research

∙ 03/24/2023

Factorizers for Distributed Sparse Block Codes

Distributed sparse block codes (SBCs) exhibit compact representations fo...

0 Michael Hersche, et al. ∙

research

∙ 02/16/2023

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Analog in-memory computing (AIMC) – a promising approach for energy-effi...

0 Malte J. Rasch, et al. ∙

research

∙ 02/04/2023

WHYPE: A Scale-Out Architecture with Wireless Over-the-Air Majority for Scalable In-memory Hyperdimensional Computing

Hyperdimensional computing (HDC) is an emerging computing paradigm that ...

0 Robert Guirado, et al. ∙

research

∙ 11/09/2022

In-memory factorization of holographic perceptual representations

Disentanglement of constituent factors of a sensory signal is central to...

0 Jovin Langenegger, et al. ∙

research

∙ 09/21/2022

Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

The massive use of artificial neural networks (ANNs), increasingly popul...

0 Dominique J. Kösters, et al. ∙

research

∙ 07/14/2022

In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory

Continually learning new classes from a few training examples without fo...

12 Geethan Karunaratne, et al. ∙

research

∙ 05/22/2022

Wireless On-Chip Communications for Scalable In-memory Hyperdimensional Computing

Hyperdimensional computing (HDC) is an emerging computing paradigm that ...

0 Robert Guirado, et al. ∙

research

∙ 05/20/2022

ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning

Analog in-memory computing (AIMC) cores offers significant performance a...

0 Joshua Klein, et al. ∙

research

∙ 03/30/2022

Constrained Few-shot Class-incremental Learning

Continually learning new classes from fresh data without forgetting prev...

0 Michael Hersche, et al. ∙

research

∙ 03/11/2022

Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks

Memory-augmented neural networks enhance a neural network with an extern...

0 Denis Kleyko, et al. ∙

research

∙ 03/09/2022

A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices

Neither deep neural networks nor symbolic AI alone have approached the k...

7 Michael Hersche, et al. ∙

research

∙ 11/10/2021

AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator

Always-on TinyML perception tasks in IoT applications require very high ...

13 Chuteng Zhou, et al. ∙

research

∙ 04/05/2021

A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays

We introduce the IBM Analog Hardware Acceleration Kit, a new and first o...

42 Malte J. Rasch, et al. ∙

research

∙ 11/08/2020

Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors

The main design principles in computer architecture have recently shifte...

0 Sergi Abadal, et al. ∙

research

∙ 10/05/2020

Robust High-dimensional Memory-augmented Neural Networks

Traditional neural networks require enormous amounts of data to build th...

1 Geethan Karunaratne, et al. ∙

research

∙ 09/15/2020

Short-term synaptic plasticity optimally models continuous environments

Biological neural networks operate with extraordinary energy efficiency,...

12 Timoleon Moraitis, et al. ∙

research

∙ 04/30/2020

Memristors – from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing

Machine learning, particularly in the form of deep learning, has driven ...

24 Adnan Mehonic, et al. ∙

research

∙ 04/08/2020

File Classification Based on Spiking Neural Networks

In this paper, we propose a system for file classification in large data...

0 Ana Stanojevic, et al. ∙

research

∙ 03/25/2020

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

Deep neural networks (DNNs) have surpassed human-level accuracy in a var...

0 Vinay Joshi, et al. ∙

research

∙ 03/05/2020

Compiling Neural Networks for a Computational Memory Accelerator

Computational memory (CM) is a promising approach for accelerating infer...

0 Kornilios Kourtis, et al. ∙

research

∙ 06/08/2019

5 Parallel Prism: A topology for pipelined implementations of convolutional neural networks using computational memory

In-memory computing is an emerging computing paradigm that could enable ...

0 Martino Dazzi, et al. ∙

research

∙ 06/04/2019

In-memory hyperdimensional computing

Hyperdimensional computing (HDC) is an emerging computing framework that...

0 Geethan Karunaratne, et al. ∙

research

∙ 05/28/2019

Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Spiking neural networks (SNN) are artificial computational models that h...

0 S. R. Nandakumar, et al. ∙

research

∙ 01/11/2019

Low-Power Neuromorphic Hardware for Signal Processing Applications

Machine learning has emerged as the dominant tool for implementing compl...

2 Bipin Rajendran, et al. ∙

research

∙ 06/17/2017

Fatiguing STDP: Learning from Spike-Timing Codes in the Presence of Rate Codes

Spiking neural networks (SNNs) could play a key role in unsupervised mac...

0 Timoleon Moraitis, et al. ∙

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