Magazine
Volume 37, Issue 2 (March/April)
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Highlights
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March/April 2020 Content
From the EIC
| • | Robust Machine Learning |
| View full article (PDF). |
Special Issue on Robust Resource-Constrained Systems for Machine Learning
| • | Guest Editors’ Introduction: Robust Resource-Constrained Systems for Machine Learning |
| View full article (PDF). |
| • | SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters |
| Training data is crucial in ensuring robust neural inference, and deep neural networks (DNNs) are heavily dependent on this assumption. However, DNNs can be exploited by adversaries that facilitate various attacks. read more View full article (PDF). |
| • | Adaptive Neural Network Architectures for Power Aware Inference |
| When dealing with edge devices, diverse power and compute constraints impose tradeoffs among performance, accuracy, and energy requirements during inference. read more. View full article (PDF). |
| • | Are CNNs Reliable Enough for Critical Applications? An Exploratory Study |
| Resource-constrained CNN implementations are subject to various reliability threats. read more. View full article (PDF). |
| • | Impact of Memory Voltage Scaling on Accuracy and Resilience of Deep Learning Based Edge Devices |
| As more and more artificial intelligence capabilities are deployed onto resource-constrained devices, designers explore several techniques in an effort to boost energy efficiency. read more View full article (PDF). |
| • | Enabling Timing Error Resilience for Low-Power Systolic-Array Based Deep Learning Accelerators |
| Hardware-accelerated learning and inference algorithms are quite popular in edge devices where predictable timing behavior and minimal energy consumption are required, while maintaining robustness to timing errors. read more View full article (PDF). |
| • | Backdoor Suppression in Neural Networks using Input Fuzzing and Majority Voting |
| While inference is needed at the edge, training is typically done at the cloud. Therefore, data necessary for training a model, as well as the trained model, have to be transmitted back and forth between the edge and the cloud training infrastructure. read more View full article (PDF). |
Survey Paper
| • | Robust Machine Learning Systems: Challenges,Current Trends, Perspectives, and the Road Ahead |
| Currently, machine learning (ML) techniques are at the heart of smart cyber-physical systems (CPS) and Internet-of-Things (IoT). read more View full article (PDF). |
Keynote Papers
| • | Semantic Adversarial Deep Learning |
| Adversarial examples have emerged as a key threat for machine-learning-based systems, especially the ones that employ deep neural networks. read more View full article (PDF). |
| • | Training Large-scale Artificial Neural Networks on Simulated Resistive Crossbar Arrays |
| Resistive crossbar arrays are promising options for accelerating enormous computation needed for training modern deep neural networks (DNNs). read more View full article (PDF). |
General Interest Paper
| • | Remote Electrical-level Security Threats to Multi-Tenant FPGAs |
| Virtualized FPGAs to provide multitenant access to increase their utilization have become a popular trend among the cloud computing providers. read more View full article (PDF). |
Departments
| • | Report on the 38th ACM/IEEE International Conference on Computer-Aided Design (ICCAD 2019) |
| View full article (PDF). |
| • | The Last Byte: Are You Sure You Love That Store? |
| View full article (PDF). |
