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Piergiulio Mannocci

Assistant professor

Politecnico di Milano

Italy

Research Interests

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Piergiulio Mannocci is an Assistant Professor at Politecnico di Milano, Italy. His research focuses on in-memory computing, including the development of advanced memory devices and architectures for machine learning applications. Recent contributions include works on seizure detection using reservoir computing and innovative approaches to resistive switching memory. He is dedicated to exploring energy-efficient solutions for high-density computing systems.

Articles (20)

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Collaborators (4)

Daniele Ielmini

Full Professor

Politecnico di Milano

ITALY

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Emanuele Gemo

RTDa

Politecnico di Torino Dipartimento di Elettronica e Telecomunicazioni

ITALY

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Fernando Corinto

Docenti di ruolo di Ia fascia

Politecnico di Torino

ITALY

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Yoeri van de Burgt

Professor at Eindhoven University of Technology

Eindhoven University of Technology

NETHERLANDS

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Piergiulio Mannocci

Research Interests

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About

Articles (20)

Technology Roadmap of Bioinspired Computing Hardware

A fully integrated analogue closed-loop in-memory computing accelerator based on static random-access memory

High-Accuracy, High-Performance In-Memory Computing With High-Resistance Spin-Orbit Torque (SOT) Magnetic Memory

Seizure detection via reservoir computing in MoS2-based charge trap memory devices

3-D Vertical Resistive Switching Random Access Memory (3D-VRRAM) With Multilevel Programming for High-Density, Energy-Efficient In-Memory Computing

Analog Circuit Design for In-Memory Linear Algebra Accelerators

Bio-Inspired Computing with Emerging Devices: Bridging 2D Materials and Neuromorphic Architectures

Emerging Materials and Computing Paradigms for Temporal Signal Analysis

In-memory reconstruction of compressively-sampled signals by nonlinear closed-loop analog circuits

Linear compensation of drift and mitigation of drift variations in PCM arrays for highly accurate, energy efficient in-memory computing

Compact Modeling and Mitigation of Parasitics in Crosspoint Accelerators of Neural Networks

In-Memory Principal Component Analysis by Analogue Closed-Loop Eigendecomposition

A Generalized Block-Matrix Circuit for Closed-Loop Analog In-Memory Computing

In-memory computing with emerging memory devices: Status and outlook

In-memory computing for machine learning and deep learning

An Analogue In-Memory Ridge Regression Circuit With Application to Massive MIMO Acceleration

Modeling and Compensation of IR Drop in Crosspoint Accelerators of Neural Networks

In-Memory Principal Component Analysis by Crosspoint Array of Resistive Switching Memory: A New Hardware Approach for Energy-Efficient Data Analysis in Edge Computing.

A Universal, Analog, In-Memory Computing Primitive for Linear Algebra Using Memristors

Redundancy and Analog Slicing for Precise In-Memory Machine Learning—Part I: Programming Techniques

Collaborators (4)

Daniele Ielmini

Emanuele Gemo

Fernando Corinto

Yoeri van de Burgt

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