Publications related to Magnetic-core memory

Decoder Partitioning: Towards Practical List Decoding of Polar Codes

Rüdiger Urbanke, Seyed Hamed Hassani, Marco Mondelli

Polar codes represent one of the major recent breakthroughs in coding theory and, because of their attractive features, they have been selected for the incoming SG standard. As such, a lot of attention has been devoted to the development of decoding algori ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Adaptive Quantization for Deep Neural Network

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Yiren Zhou

In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNNs generally comes with high computational costs and large memory consumption ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

Nonreciprocity in Microwave Optomechanical Circuits

Tobias Kippenberg, Nathan Rafaël Bernier, László Dániel Tóth

Nonreciprocal devices such as isolators and circulators are necessary to protect sensitive apparatus from unwanted noise. Recently, a variety of alternatives were proposed to replace ferrite-based commercial technologies, with the motivation to be integrat ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

A phase-change memory model for neuromorphic computing

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Phase-change memory (PCM) is an emerging non-volatile memory technology that is based on the reversible and rapid phase transition between the amorphous and crystalline phases of certain phase-change materials. The ability to alter the conductance levels i ...

AMER INST PHYSICS2018

Adaptive Cache Mode Selection for Queries over Raw Data

Anastasia Ailamaki, Tahir Azim, Azqa Nadeem

Caching the results of intermediate query results for future re-use is a common technique for improving the performance of analytics over raw data sources. An important design choice in this regard is whether to lazily cache only the offsets of satisfying ...

2018

Impact of conductance drift on multi-PCM synaptic architectures

Yusuf Leblebici, Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

In-memory computing with nanoscale memristive devices such as phase-change memory (PCM) has emerged as an alternative to conventional von Neumann systems to train deep neural networks (DNN) where a synaptic weight is represented by the device conductance. ...

IEEE2018

Error Tolerance Analysis of Deep Learning Hardware Using a Restricted Boltzmann Machine Toward Low-Power Memory Implementation

Alexandre Schmid, Takao Marukame, Kodai Ueyoshi

Remarkable hardware robustness of deep learning (DL) is revealed by error injection analyses performed using a custom hardware model implementing parallelized restricted Boltzmann machines (RBMs). RBMs in deep belief networks demonstrate robustness against ...

Ieee-Inst Electrical Electronics Engineers Inc2017

Reducing circuit design complexity for neuromorphic machine learning systems based on Non-Volatile Memory arrays

Yusuf Leblebici, Alessandro Fumarola

Machine Learning (ML) is an attractive application of Non-Volatile Memory (NVM) arrays [1,2]. However, achieving speedup over GPUs will require minimal neuron circuit sharing and thus highly area-efficient peripheral circuitry, so that ML reads and writes ...

2017

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Magnetic-core memory

Decoder Partitioning: Towards Practical List Decoding of Polar Codes

Resilience of riverbed vegetation to uprooting by flow

Adaptive Quantization for Deep Neural Network

Nonreciprocity in Microwave Optomechanical Circuits

A phase-change memory model for neuromorphic computing

Adaptive Cache Mode Selection for Queries over Raw Data

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

Impact of conductance drift on multi-PCM synaptic architectures

Error Tolerance Analysis of Deep Learning Hardware Using a Restricted Boltzmann Machine Toward Low-Power Memory Implementation

Reducing circuit design complexity for neuromorphic machine learning systems based on Non-Volatile Memory arrays

Graph Chatbot

Chat with Graph Search

Adaptive Cache Mode Selection for Queries over Raw Data

Resilience of riverbed vegetation to uprooting by flow

Adaptive Quantization for Deep Neural Network

A phase-change memory model for neuromorphic computing

Decoder Partitioning: Towards Practical List Decoding of Polar Codes

Nonreciprocity in Microwave Optomechanical Circuits

Impact of conductance drift on multi-PCM synaptic architectures

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

Error Tolerance Analysis of Deep Learning Hardware Using a Restricted Boltzmann Machine Toward Low-Power Memory Implementation

Reducing circuit design complexity for neuromorphic machine learning systems based on Non-Volatile Memory arrays