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Accurate deep neural network inference using computational phase-change memory

Publications associées (33)

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Here we use triple-cation metal-organic halide perovskite single crystals for the transistor channel of a flash memory device. Moreover, we design and demonstrate a 10 nm thick single-layer nanofloating gate. It consists of a ternary blend of two organic s ...

2020

Phase-Change Memory Models for Deep Learning Training and Inference

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Non-volatile analog memory devices such as phase-change memory (PCM) enable designing dedicated connectivity matrices for the hardware implementation of deep neural networks (DNN). In this in-memory computing approach, the analog conductance states of the ...

IEEE2019

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In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices, Here, we present an overview of the application of in ...

IEEE2019

A Product Engine for Energy-Efficient Execution of Binary Neural Networks Using Resistive Memories

David Atienza Alonso, Marina Zapater Sancho, Pierre-Emmanuel Julien Marc Gaillardon, Xifan Tang, Yasir Mahmood Qureshi, Edouard Giacomin, João Miguel Morgado Pereira Vieira

The need for running complex Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs), in edge devices, which are highly constrained in terms of computing power and energy, makes it important to execute such applications efficiently. ...

2019

ReRAM From Material Study to CMOS Co-integration

Elmira Shahrabi

The revolution of information-technology owes to silicon-based complementary-metal-oxide (CMOS) technology. However, CMOS technology approaches its physical limitation hardening the further progress of memory devices as well as computing paradigm requiring ...

EPFL2019

Training fully connected networks with resistive memories: impact of device failures

Giorgio Cristiano, Massimo Giordano, Martina Bodini

Hardware accelerators based on two-terminal non-volatile memories (NVMs) can potentially provide competitive speed and accuracy for the training of fully connected deep neural networks (FC-DNNs), with respect to GPUs and other digital accelerators. We rece ...

2019

Resistive Random Access Memories (ReRAMs) have been researched intensively in the last past decades as a promising alternatives technology for the next-generation non-volatile memory (NVM) devices. ReRAMâs excellent performance properties such as high sw ...

EPFL2019

Perspective on training fully connected networks with resistive memories: Device requirements for multiple conductances of varying significance

Giorgio Cristiano, Massimo Giordano

Novel Deep Neural Network (DNN) accelerators based on crossbar arrays of non-volatile memories (NVMs)-such as Phase-Change Memory or Resistive Memory-can implement multiply-accumulate operations in a highly parallelized fashion. In such systems, computatio ...

2018

With the continuous scaling of CMOS technology, integrating an embedded high-density non-volatile memory appears to be more and more costly and technologically challenging. Beyond floating-gate memory technologies, bipolar resistive random access memories ...

2018

Mixed-precision architecture based on computational memory for training deep neural networks

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Deep neural networks (DNN) have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image or speech recognition. Training of large DNNs, however, is a computationally in ...

IEEE2018