A Pruning Technique for B&B Based Design Exploration of Approximate Computing Variants

2016 ◽  
Author(s):  
Mario Barbareschi ◽  
Federico Iannucci ◽  
Antonino Mazzeo
Keyword(s):  
Approximate Computing ◽  
Design Exploration ◽  
Pruning Technique

scholarly journals Machine Learning-Based Pruning Technique for Low Power Approximate Computing

2022 ◽  
Vol 42 (1) ◽  
pp. 397-406
Author(s):  
B. Sakthivel ◽  
K. Jayaram ◽  
N. Manikanda Devarajan ◽  
S. Mahaboob Basha ◽  
S. Rajapriya
Keyword(s):  
Machine Learning ◽  
Low Power ◽  
Approximate Computing ◽  
Pruning Technique

Approximate computing design exploration through data lifetime metrics

2019 ◽  
Author(s):  
Alessandro Savino ◽  
Michele Portolan ◽  
Regis Leveugle ◽  
Stefano Di Carlo
Keyword(s):  
Approximate Computing ◽  
Design Exploration ◽  
Data Lifetime

A Design Exploration Framework for Secure IoT-Systems

2020 ◽  
Author(s):  
Lukas Gressl ◽  
Alexander Rech ◽  
Christian Steger ◽  
Andreas Sinnhofer ◽  
Ralph Weissnegger
Keyword(s):  
Design Exploration

On Using Approximate Computing to Build an Error Detection Scheme for Arithmetic Circuits

2020 ◽  
Vol 36 (1) ◽  
pp. 33-46
Author(s):  
B. Deveautour ◽  
A. Virazel ◽  
P. Girard ◽  
V. Gherman
Keyword(s):  
Error Detection ◽  
Arithmetic Circuits ◽  
Approximate Computing ◽  
Detection Scheme

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

Security Threat Analyses and Attack Models for Approximate Computing Systems

2021 ◽  
Vol 26 (4) ◽  
pp. 1-31
Author(s):  
Pruthvy Yellu ◽  
Landon Buell ◽  
Miguel Mark ◽  
Michel A. Kinsy ◽  
Dongpeng Xu ◽  
...  
Keyword(s):  
Error Resilience ◽  
Security Threats ◽  
Approximate Computing ◽  
Security Threat ◽  
Security Vulnerabilities ◽  
Computing Systems ◽  
Quantitative Analyses ◽  
Resilience Mechanisms ◽  
The Impact ◽  
Attack Models

Approximate computing (AC) represents a paradigm shift from conventional precise processing to inexact computation but still satisfying the system requirement on accuracy. The rapid progress on the development of diverse AC techniques allows us to apply approximate computing to many computation-intensive applications. However, the utilization of AC techniques could bring in new unique security threats to computing systems. This work does a survey on existing circuit-, architecture-, and compiler-level approximate mechanisms/algorithms, with special emphasis on potential security vulnerabilities. Qualitative and quantitative analyses are performed to assess the impact of the new security threats on AC systems. Moreover, this work proposes four unique visionary attack models, which systematically cover the attacks that build covert channels, compensate approximation errors, terminate normal error resilience mechanisms, and propagate additional errors. To thwart those attacks, this work further offers the guideline of countermeasure designs. Several case studies are provided to illustrate the implementation of the suggested countermeasures.

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