Optimization of Dynamic Memory Managers for Embedded Systems Using Grammatical Evolution

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Method for reading and writing unreliable memories and a corresponding memory controller device and memory

Andreas Peter Burg, Reza Ghanaatian Jahromi

A method of accessing a memory space of a memory device with a decoder, the memory space having faults, including the steps of performing a memory access operation by an electronic device to a access a logical memory space of the memory device, and randomi ...

2022

In-Memory Hardware and Architectural Extensions for Workloads Acceleration

William Andrew Simon

Utilization of edge devices has exploded in the last decade, with such use cases as wearable devices, autonomous driving, and smart homes. As their ubiquity grows, so do expectations of their capabilities. Simultaneously, their formfactor and use cases lim ...

EPFL2022

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Flavio Ponzina

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

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

HPCache: Memory-Efficient OLAP Through Proportional Caching

Anastasia Ailamaki, Hamish Mcniece Hill Nicholson, Periklis Chrysogelos

Analytical engines rely on in-memory caching to avoid disk accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- & time-based caching decisions, however, are a proxy of the expected query execution ...

ACM2022

Error Resilient In-Memory Computing Architecture for CNN Inference on the Edge

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

The growing popularity of edge computing has fostered the development of diverse solutions to support Artificial Intelligence (AI) in energy-constrained devices. Nonetheless, comparatively few efforts have focused on the resiliency exhibited by AI workload ...

2022

Impact of Interface Traps in Floating-Gate Memory Based on Monolayer MoS2

Andras Kis, Guilherme Migliato Marega

Two-dimensional materials (2DMs) have found potential applications in many areas of electronics, such as sensing, memory systems, optoelectronics, and power. Despite an intense experimental work, the literature is lacking of accurate modeling of nonvolatil ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang, Ali Pahlevan

Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of th ...

2022

Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2

Aleksandra Radenovic, Andras Kis, Mukesh Kumar Tripathi, Guilherme Migliato Marega, Zhenyu Wang

Machine learning and signal processing on the edge are poised to influence our everyday lives with devices that will learn and infer from data generated by smart sensors and other devices for the Internet of Things. The next leap toward ubiquitous electron ...

2022

Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities

Alexandre Schmid, Lizeth Gonzalez Carabarin

Unstructured neural network pruning algorithms have achieved impressive compression ratios. However, the resulting-typically irregular-sparse matrices hamper efficient hardware implementations, leading to additional memory usage and complex control logic t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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