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Impact of Memory Voltage Scaling on Accuracy and Resilience of Deep Learning Based Edge Devices

Publications associées (36)

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EdgeAI-Aware Design of In-Memory Computing Architectures

Marco Antonio Rios

Driven by the demand for real-time processing and the need to minimize latency in AI algorithms, edge computing has experienced remarkable progress. Decision-making AI applications stand out for their heavy reliance on data-centric operations, predominantl ...

EPFL2024

Low Power LDPC Decoding by Reliable Voltage Down-Scaling

Andreas Peter Burg

Low-Density Parity-Check (LDPC) decoder is among the power hungry building blocks of wireless communication systems. Voltage scaling down to Near-Threshold (NT) voltages substantially improves energy efficiency, in theory up 10x. However, tuning the voltag ...

New York2023

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

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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

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Resistive switching memory technologies (RRAM) are seen by most of the scientific community as an enabler for Edge-level applications such as embedded deep Learning, AI or signal processing of audio and video signals. However, going beyond a "simple'' repl ...

IEEE2020

As an increase of intelligent and self-powered devices is forecasted for our future everyday life, the implementation of energy-autonomous devices that can wirelessly communicate data from sensors is crucial. Even though techniques such as voltage scaling ...

EPFL2019

Buildings account for 35% of the global final energy demand. Efficiency improvements and advanced control strategies have a significant impact in the reduction of energy costs and CO2 emissions. Building energy simulation is widely used to help planners, c ...

2019

Smart health monitoring devices, known as Wireless Body Sensor Nodes (WBSN) are transforming today's health-care landscape, shifting it from traditional hospital-based methods toward more personalized approaches. Their demand is ever-increasing in the mode ...

EPFL2019

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

A 24 kb Single-Well Mixed 3T Gain-Cell eDRAM with Body-Bias in 28 nm FD-SOI for Refresh-Free DSP Applications

Andreas Peter Burg, Robert Giterman, Cosimo Aprile, Yusuf Leblebici, Andrea Bonetti, Jonathan Emmanuel D Narinx, Nicolas Claude Frigerio

Logic-compatible gain-cell embedded DRAM (GC-eDRAM) is an emerging alternative to conventional SRAM for memory-dominated system-on-chip (SoC) designs due to its high-density, low-power, and two-ported operation. Although GCs have a limited data retention t ...

IEEE2019