Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework ICCAD Special Session Paper
Open accessAbstract
The security and privacy concerns along with the amount of data that is required to be processed on regular basis has pushed processing to the edge of the computing systems. Deploying advanced Neural Networks (NN), such as deep neural networks (DNNs) and spiking neural networks (SNNs), that offer state-of-the-art results on resource-constrained edge devices is challenging due to the stringent memory and power/energy constraints. Moreover, these systems are required to maintain correct functionality under diverse security and reliability threats. This paper first discusses existing approaches to address energy efficiency, reliability, and security issues at different system layers, i.e., hardware (HW) and software (SW). Afterward, we discuss how to further improve the performance (latency) and the energy efficiency of Edge AI systems through HW/SW-level optimizations, such as pruning, quantization, and approximation. To address reliability threats (like permanent and transient faults), we highlight cost-effective mitigation techniques, like fault-aware training and mapping. Moreover, we briefly discuss effective detection and protection techniques to address security threats (like model and data corruption). Towards the end, we discuss how these techniques can be combined in an integrated cross-layer framework for realizing robust and energy-efficient Edge AI systems.
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Edge AI processes and analyses data locally, in real time, at the edge of the network rather than transmitting it to a remote server.
“This means that data is processed and analysed locally, in real-time, at the edge of the network, rather than being transmitted to a remote server for processing [185].”
From Edge AI: A survey (2023) · cites this paper as [185]
Section II (p. 2) describes efficient, real-time data processing at the edge, and the Key Challenges section (p. 1) says Edge AI systems are expected to provide real-time responses. The copy does not explicitly say that data is processed locally instead of being transmitted to a remote server, so that contrast is not fully supported. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSome possible solutions exist via AI Model Efficiency Toolkit (AIMET) [143], which blends AI trained algorithm with compression and quantization leading to optimization of ML or DL model while maintaining the same accuracy.
“Some possible solutions exist via AI Model Efficiency Toolkit (AIMET) [143], which blends the AI trained algorithm with compression and quantization which leads to the optimization of the ML or DL model while maintaining the same accuracy.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [143]
The 'Optimizations for DNN Models' section (p. 2) says pruning and quantization can reduce model size and computation. It does not identify AIMET as the toolkit providing these optimizations, and its quantization example reports a small accuracy degradation rather than unchanged accuracy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredReliability is an important metric of adoption of TinyML systems for large-scale real-life use cases.
“It is an important metric of adoption of TinyML systems for large-scale real-life use cases (Shafique et al., 2021).”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Shafique et al., 2021
The Introduction (p. 1) names reliability as an important design metric for enabling Edge AI, and the Conclusion (p. 7) says robustness, including reliability, matters as Edge AI and tinyML use grows. The paper does not specifically assess reliability as a metric of adoption for large-scale real-life TinyML deployments. (LLM, conf=0.82)
Experimental — accuracy not yet measuredShafique et al. (2021) present a cross-layer optimization framework to consider accuracy and performance for exploring architectural space and to generate a suitable Pareto-optimal deep neural network model.
“A cross-layer optimization framework is presented in (Shafique et al., 2021) to consider the accuracy and performance for exploring architectural space.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Shafique et al., 2021
By keeping data on-premises, Edge AI protects users' confidentiality, sending only the results of analyses and observations to the cloud.
“By keeping such data on-premises, Edge AI additionally protects users' confidentiality, sending just the results of their analyses and observations to the cloud [185].”
From Edge AI: A survey (2023) · cites this paper as [185]
The security discussion (Section III, pp. 3–5) addresses vulnerabilities and protection against attacks, but does not describe keeping users’ data on-premises or sending only analysis results to the cloud. Searches for local processing, confidentiality, and cloud transmission found no passage establishing that data-sharing arrangement. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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Putra, R. V. W., Hanif, M. A., & Shafique, M. (2021) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2021.3060509 · Cited by 32 · see citing works
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Self-citationNeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips
Venceslai, V., Marchisio, A., Alouani, I., Martina, M., & Shafique, M. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207351 · Cited by 29 · see citing works
Self-citationSrinivasan, G., Roy, S., Raghunathan, V., & Roy, K. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966075 · Cited by 28 · see citing works
Putra, R. V. W., Hanif, M. A., & Shafique, M. (2021) · Proceedings - ACM IEEE Design Automation Conference · doi:10.1109/dac18074.2021.9586332 · Cited by 28 · see citing works
Self-citationHanif, M. A., Khalid, F., & Shafique, M. (2019) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3316781.3317787 · Cited by 27 · see citing works
Self-citationA Programmable Event-driven Architecture for Evaluating Spiking Neural Networks
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Neuroattack:undermining spiking neural networks security through externally triggered bit-flips
Venceslai, V. (., Marchisio, A. (., Alouani, I., Martina, M. (., & Shafique, M. (. (2020) · LillOA (Université de Lille (University Of Lille)) · Cited by 26 · see citing works
Self-citationTrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks
Khalid, F., Hanif, M. A., Rehman, S., Ahmed, R., & Shafique, M. (2019) · IEEE International On-Line Testing Symposium (IOLTS) · doi:10.1109/iolts.2019.8854425 · Cited by 24 · see citing works
Self-citationHardware Obfuscation and Logic Locking: A Tutorial Introduction
Hoque, T., Chakraborty, R. S., & Bhunia, S. (2020) · IEEE Design and Test · doi:10.1109/mdat.2020.2984224 · Cited by 24 · see citing works
Kriebel, F., Rehman, S., Hanif, M. A., Khalid, F., & Shafique, M. (2018) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi.2018.00111 · Cited by 23 · see citing works
Self-citationFaDec: A Fast Decision-based Attack for Adversarial Machine Learning
Khalid, F., Ali, H. M., Hanif, M. A., Rehman, S., Ahmed, R., & Shafique, M. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207635 · Cited by 23 · see citing works
Self-citationPruNet: Class-Blind Pruning Method For Deep Neural Networks
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Self-citationCapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule NetworksPreprint
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Self-citationSupervised machine learning using encrypted training data
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Self-citationZatt, B., Shafique, M., Bampi, S., & Henkel, J. (2011) · doi:10.1109/iccad.2011.6105303 · Cited by 18 · see citing works
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Self-citationFANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks
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Self-citationHanif, M. A., & Shafique, M. (2021) · Design, Automation, and Test in Europe · doi:10.23919/date51398.2021.9473943 · Cited by 17 · see citing works
Self-citationMarchisio, A., Pira, G., Martina, M., Masera, G., & Shafique, M. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636718 · Cited by 17 · see citing works
Self-citationDESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware
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Self-citationMulti-Bank On-Chip Memory Management Techniques for CNN Accelerators
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Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks
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Shafique, M., Khan, M. U. K., Tufek, A. O., & Henkel, J. (2015) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2744769.2744834 · Cited by 12 · see citing works
Self-citationBus Width Aware Off-Chip Memory Access Minimization for CNN Accelerators
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PANDA: Facilitating Usable AI DevelopmentPreprint
Gao, J., Wang, W., Zhang, M., Chen, G., Jagadish, H. V., Li, G., Ng, T. K., Ooi, B. C., Wang, S., & Zhou, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.09997 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [40][40] M. A. Hanif, F. Khalid, and M. Shafique, 'Cann: Curable approximations for high-performance deep neural network accelerators,' in Proc. of DAC , 2019, pp. 1-6. -
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- Authors
- Shafique, M., Marchisio, A., Wicaksana Putra, R. V., & Hanif, M. A.
- Affiliations
- New York University Abu Dhabi (AE)
- TU Wien (AT)
- Venue
- 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)
- Publisher
- IEEE
- Published
- 2021-11-01
- Pages
- 1-9
- OpenAlex
- W4200376271
- Cited by
- 58
- References
- 112
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 7 · 2023: 19 · 2022: 12 · 2021: 1
- FWCI
- 9.01
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- edge AI, energy efficiency, cross-layer framework, hardware-software co-optimization, deep neural network, spiking neural networks, neural network pruning, quantization, neural network approximation, fault tolerance, privacy, latency
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