898 citations · 1.6k across the 10 of their papers we have counts for
12 papers
Cryogenic In-Memory Computing with Phase-Change Memory
Davide G. F. Lombardo, Siddharth Gautam, Alberto Ferraris +3
In-memory computing (IMC) is an emerging non-von Neumann paradigm that leverages the intrinsic physics of memory devices to perform computations directly within the memory array. A…
Analog Foundation Models
Julian Büchel, Iason Chalas, Giovanni Acampa +7
Analog in-memory computing (AIMC) is a promising compute paradigm to improve speed and power efficiency of neural network inference beyond the limits of conventional von Neumann-ba…
Roadmap to Neuromorphic Computing with Emerging Technologies
Adnan Mehonic, Daniele Ielmini, Kaushik Roy +50
The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently util…
Gradient descent-based programming of analog in-memory computing cores
Julian Büchel, Athanasios Vasilopoulos, Benedikt Kersting +11
The precise programming of crossbar arrays of unit-cells is crucial for obtaining high matrix-vector-multiplication (MVM) accuracy in analog in-memory computing (AIMC) cores. We pr…
Measurement of onset of structural relaxation in melt-quenched phase change materials
Benedikt Kersting, Syed Ghazi Sarwat, Manuel Le Gallo +5
Chalcogenide phase change materials enable non-volatile, low-latency storage-class memory. They are also being explored for new forms of computing such as neuromorphic and in-memor…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing
Anastasios Petropoulos, Irem Boybat, Manuel Le Gallo +3
In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory…
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Vinay Joshi, Geethan Karunaratne, Manuel Le Gallo +5
Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits…
Accurate deep neural network inference using computational phase-change memory
Vinay Joshi, Manuel Le Gallo, Simon Haefeli +7
In-memory computing is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. Crossbar arrays of resistive memory devices can be used to…
Mixed-precision training of deep neural networks using computational memory
Nandakumar S. R., Manuel Le Gallo, Irem Boybat +3
Deep neural networks have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image and speec…
Neuromorphic computing with multi-memristive synapses
Irem Boybat, Manuel Le Gallo, S. R. Nandakumar +7
Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which…
Temporal correlation detection using computational phase-change memory
Abu Sebastian, Tomas Tuma, Nikolaos Papandreou +4
For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units…