Publications related to Magnetic-core memory

Magnon-Assisted Magnetization Reversal of Ni81Fe19 Nanostripes on Y3Fe5O12 with Different Interfaces

Dirk Grundler, Andrea Mucchietto, Korbinian Baumgärtl

Magnetic bit writing by short-wave magnons without conversion to the electrical domain is expected to be a game-changer for in-memory computing architectures. Recently, the reversal of nanomagnets by propagating magnons was demonstrated. However, experimen ...

2024

A 128-kbit GC-eDRAM With Negative Boosted Bootstrap Driver for 11.3x Lower-Refresh Frequency at a 2.5% Area Overhead in 28-nm FD-SOI

Andreas Peter Burg, Robert Giterman, Halil Andac Yigit, Emmanuel Nieto Casarrubias

Gain-cell embedded DRAM (GC-eDRAM) is a high-density logic-compatible alternative to conventional static random-access memory (SRAM) and embedded DRAM (eDRAM). However, GC-eDRAM suffers from a reduced data retention time (DRT) at deeply-scaled process node ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Marco Antonio Rios, Flavio Ponzina

By supporting the access of multiple memory words at the same time, Bit-line Computing (BC) architectures allow the parallel execution of bit-wise operations in-memory. At the array periphery, arithmetic operations are then derived with little additional o ...

2023

Chaosity: Understanding Contemporary NUMA-architectures

Anastasia Ailamaki, Viktor Sanca, Hamish Mcniece Hill Nicholson, Andreea Nica, Syed Mohammad Aunn Raza

Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

2023

HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics

Anastasia Ailamaki, Periklis Chrysogelos, Hamish Mcniece Hill Nicholson, Syed Mohammad Aunn Raza

Accessing input data is a critical operation in data analytics: i) slow data access significantly degrades performance, and ii) storing everything in the fastest medium, i.e., memory, incurs high operational and hardware costs. Further, while GPUs offer in ...

2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

AstriFlash: A Flash-Based System for Online Services

Babak Falsafi, Lei Yan, Siddharth Gupta, Mark Johnathon Sutherland, Yunho Oh

Modern datacenters host datasets in DRAM to offer large-scale online services with tight tail-latency requirements. Unfortunately, as DRAM is expensive and increasingly difficult to scale, datacenter operators are forced to consider denser storage technolo ...

2022

Design for Test With Unreliable Memories by Restoring the Beauty of Randomness

Andreas Peter Burg, Reza Ghanaatian Jahromi, Marco Widmer

This article presents a design-for-test methodology for embedded memories. The methodology relies on a fully random fault model of post-fabrication errors, which results in a low-overhead test strategy. The methodology's effectiveness is demonstrated on an ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Magnetic-core memory

Magnon-Assisted Magnetization Reversal of Ni81Fe19 Nanostripes on Y3Fe5O12 with Different Interfaces

A 128-kbit GC-eDRAM With Negative Boosted Bootstrap Driver for 11.3x Lower-Refresh Frequency at a 2.5% Area Overhead in 28-nm FD-SOI

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

Chaosity: Understanding Contemporary NUMA-architectures

HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale

In-Memory Hardware and Architectural Extensions for Workloads Acceleration

AstriFlash: A Flash-Based System for Online Services

Design for Test With Unreliable Memories by Restoring the Beauty of Randomness

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Chat with Graph Search

A 128-kbit GC-eDRAM With Negative Boosted Bootstrap Driver for 11.3x Lower-Refresh Frequency at a 2.5% Area Overhead in 28-nm FD-SOI

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

Chaosity: Understanding Contemporary NUMA-architectures

HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics

Magnon-Assisted Magnetization Reversal of Ni81Fe19 Nanostripes on Y3Fe5O12 with Different Interfaces

Design for Test With Unreliable Memories by Restoring the Beauty of Randomness

In-Memory Hardware and Architectural Extensions for Workloads Acceleration

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale

AstriFlash: A Flash-Based System for Online Services