11.8 Hot Topic Session: Biologically-inspired techniques for smart, secure and low power SoCs

Time	Label	Presentation Title Authors
14:00	11.8.1	AN EVOLUTIONARY APPROACH TO RUNTIME VARIABILITY MAPPING AND MITIGATION ON A MULTI-RECONFIGURABLE ARCHITECTURE Speaker: Simon Bale, University of York, GB Authors: Simon Bale, Pedro Campos, Martin Albrecht Trefzer, James Walker and Andy Tyrrell, University of York, GB Abstract Intrinsic device variability has become a significant problem in deep sub-micron technology nodes. The stochastic variations in device performance, which are a result of structural irregularities at the atomic scale, can impact both the yield and reliability of a circuit design. In this paper we describe a novel multi-reconfigurable FPGA architecture, the programmable analogue and digital array (PAnDA), which can tackle this problem by allowing post-fabrication reconfiguration of the effective transistor gate widths in a circuit. We demonstrate the advantages of this architecture by creating a frequency variability map of the array using ring oscillators in order to ascertain the location of any frequency outliers. We then show that it is possible, using an evolutionary algorithm, to select alternative transistor configurations which minimise the difference in frequency between one of these outliers and the chips median frequency of operation. Such methods can be used to increase system performance and reliability by presenting an array with more uniform performance characteristics. Download Paper (PDF; Only available from the DATE venue WiFi)
14:18	11.8.2	TOWARDS LOW POWER APPROXIMATE DCT ARCHITECTURE FOR HEVC STANDARD Speaker: Zdenek Vasicek, Brno University of Technology, CZ Authors: Zdenek Vasicek, Vojtech Mrazek and Lukas Sekanina, Brno University of Technology, CZ Abstract Video processing performed directly on IoT nodes is one of the most performance as well as energy demanding applications for current IoT technology. In order to support real-time high-definition video, energy-reduction optimizations have to be introduced at all levels of the video processing chain. This paper deals with an efficient implementation of Discrete Cosine Transform (DCT) blocks employed in video compression based on the High Efficiency Video Coding (HEVC) standard. The proposed multiplierless 4-input DCT implementations contain approximate adders and subtractors that were obtained using genetic programming. In order to manage the complexity of evolutionary approximation and provide formal guarantees in terms of errors of key circuit components, the worst and average errors were determined exactly by means of Binary decision diagrams. Under conditions of our experiments, approximate 4-input DCTs show better quality/power trade-offs than relevant implementations available in the literature. For example, 25% power reduction for the same error was obtained in comparison with a recent highly optimized implementation. Download Paper (PDF; Only available from the DATE venue WiFi)
14:36	11.8.3	SEMANTIC DRIVEN HIERARCHICAL LEARNING FOR ENERGY-EFFICIENT IMAGE CLASSIFICATION Speaker: Priyadarshini Panda, Purdue University, US Authors: Priyadarshini Panda and Kaushik Roy, Purdue University, US Abstract Machine-learning algorithms have shown outstanding image recognition performance for computer vision applications. While these algorithms are modeled to mimic brain-like cognitive abilities, they lack the remarkable energy-efficient processing capability of the brain. Recent studies in neuroscience reveal that the brain resolves the competition among multiple visual stimuli presented simultaneously with several mechanisms of visual attention that are key to the brain's ability to perform cognition efficiently. One such mechanism known as saliency based selective attention simplifies complex visual tasks into characteristic features and then selectively activates particular areas of the brain based on the feature (or semantic) information in the input. Interestingly, we note that there is a significant similarity among underlying characteristic semantics (like color or texture) of images across multiple objects in real world applications. This presents us with an opportunity to decompose a large classification problem into simpler tasks based on semantic or feature similarity. In this paper, we propose semantic driven hierarchical learning to construct a tree-based classifier inspired by the biological visual attention mechanism for optimizing energy-efficiency of machine learning classifiers. We exploit the inherent feature similarity across images to identify the input variability and use recursive optimization procedure, to determine data partitioning at each tree node, thereby, learning the feature hierarchy. A set of binary classifiers is organized on top of the learnt hierarchy to minimize the overall test-time complexity. The feature based-learning allows selective activation of only those branches and nodes of the classification tree that are relevant to the input while keeping the remaining nodes idle. The proposed framework has been evaluated on Caltech-256 dataset and achieves 3.7x reduction in test complexity for 1.2% accuracy improvement over state-of-the-art one-vs-all tree-based method, and even higher improvements in test-time (of 5.5x) when some loss in output accuracy (up to 2.5%) is acceptable. Download Paper (PDF; Only available from the DATE venue WiFi)
14:54	11.8.4	MACHINE LEARNING FOR RUN-TIME ENERGY OPTIMISATION IN MANY-CORE SYSTEMS Speaker: Rishad Shafik, Newcastle University, GB Authors: Dwaipayan Biswas¹, Vibishna Balagopal¹, Rishad Shafik², Bashir Al-Hashimi¹ and Geoff Merrett¹ ¹University of Southampton, GB; ²Newcastle University, GB Abstract In recent years, the focus of computing has moved away from performance-centric serial computation to energy-efficient parallel computation. This necessitates run-time optimisation techniques to address the dynamic resource requirements of different applications on many-core architectures. In this paper, we report on intelligent run-time algorithms which have been experimentally validated for managing energy and application performance in many-core embedded system. The algorithms are underpinned by a cross-layer system approach where the hardware, system software and application layers work together to optimise the energy-performance trade-off. Algorithm development is motivated by the biological process of how a human brain (acting as an agent) interacts with the external environment (system) changing their respective states over time. This leads to a pay-off for the action taken, and the agent eventually learns to take the optimal/best decisions in future. In particular, our online approach uses a model-free reinforcement learning algorithm that suitably selects the appropriate voltage-frequency scaling based on workload prediction to meet the applications' performance requirements and achieve energy savings of up to 16% in comparison to state-of-the-art-techniques, when tested on four ARM A15 cores of an ODROID-XU3 platform. Download Paper (PDF; Only available from the DATE venue WiFi)
15:12	11.8.5	AN EVOLUTIONARY APPROACH TO HARDWARE ENCRYPTION AND TROJAN-HORSE MITIGATION Speaker: Ernesto Sanchez, Politecnico di Torino, IT Authors: Andrea Marcelli, Marco Restifo, Ernesto Sanchez and Giovanni Squillero, Politecnico di Torino, IT Abstract New threats, grouped under the name of hardware attacks, became a serious concern in recent years. In a global market, untrusted parties in the supply chain may jeopardize the production of integrated circuits with intellectual-property piracy, illegal overproduction and hardware Trojan-horses (HT) injection. While one way to protect from overproduction is to encrypt the design by inserting logic gates that prevents the circuit from generating the correct outputs unless the right key is used, reducing the number of poorly-controllable signals is known to minimize the chances for an attacker to successfully hide the trigger for some malicious payload. Several approaches successfully tackled independently these two issues. This paper proposes a novel technique based on a multi-objective evolutionary algorithm able to increase hardware security by explicitly targeting both the minimization of rare signals and the maximization of the efficacy of logic encryption. Experimental results demonstrate the proposed method is effective in creating a secure encryption schema for all the circuits under test and in reducing the number rare signals on six circuits over nine, outperforming the current state of the art. Download Paper (PDF; Only available from the DATE venue WiFi)
15:30		End of session Coffee Break in Exhibition Area On all conference days (Tuesday to Thursday), coffee and tea will be served during the coffee breaks at the below-mentioned times in the exhibition area. Tuesday, March 28, 2017 Coffee Break 10:30 - 11:30 Coffee Break 16:00 - 17:00 Wednesday, March 29, 2017 Coffee Break 10:00 - 11:00 Coffee Break 16:00 - 17:00 Thursday, March 30, 2017 Coffee Break 10:00 - 11:00 Coffee Break 15:30 - 16:00

Time

Label

Presentation Title
Authors

14:00

11.8.1

AN EVOLUTIONARY APPROACH TO RUNTIME VARIABILITY MAPPING AND MITIGATION ON A MULTI-RECONFIGURABLE ARCHITECTURE
Speaker:
Simon Bale, University of York, GB
Authors:
Simon Bale, Pedro Campos, Martin Albrecht Trefzer, James Walker and Andy Tyrrell, University of York, GB
Abstract
Intrinsic device variability has become a significant problem in deep sub-micron technology nodes. The stochastic variations in device performance, which are a result of structural irregularities at the atomic scale, can impact both the yield and reliability of a circuit design. In this paper we describe a novel multi-reconfigurable FPGA architecture, the programmable analogue and digital array (PAnDA), which can tackle this problem by allowing post-fabrication reconfiguration of the effective transistor gate widths in a circuit. We demonstrate the advantages of this architecture by creating a frequency variability map of the array using ring oscillators in order to ascertain the location of any frequency outliers. We then show that it is possible, using an evolutionary algorithm, to select alternative transistor configurations which minimise the difference in frequency between one of these outliers and the chips median frequency of operation. Such methods can be used to increase system performance and reliability by presenting an array with more uniform performance characteristics.
Download Paper (PDF; Only available from the DATE venue WiFi)

14:18

11.8.2

TOWARDS LOW POWER APPROXIMATE DCT ARCHITECTURE FOR HEVC STANDARD
Speaker:
Zdenek Vasicek, Brno University of Technology, CZ
Authors:
Zdenek Vasicek, Vojtech Mrazek and Lukas Sekanina, Brno University of Technology, CZ
Abstract
Video processing performed directly on IoT nodes is one of the most performance as well as energy demanding applications for current IoT technology. In order to support real-time high-definition video, energy-reduction optimizations have to be introduced at all levels of the video processing chain. This paper deals with an efficient implementation of Discrete Cosine Transform (DCT) blocks employed in video compression based on the High Efficiency Video Coding (HEVC) standard. The proposed multiplierless 4-input DCT implementations contain approximate adders and subtractors that were obtained using genetic programming. In order to manage the complexity of evolutionary approximation and provide formal guarantees in terms of errors of key circuit components, the worst and average errors were determined exactly by means of Binary decision diagrams. Under conditions of our experiments, approximate 4-input DCTs show better quality/power trade-offs than relevant implementations available in the literature. For example, 25% power reduction for the same error was obtained in comparison with a recent highly optimized implementation.
Download Paper (PDF; Only available from the DATE venue WiFi)

14:36

11.8.3

SEMANTIC DRIVEN HIERARCHICAL LEARNING FOR ENERGY-EFFICIENT IMAGE CLASSIFICATION
Speaker:
Priyadarshini Panda, Purdue University, US
Authors:
Priyadarshini Panda and Kaushik Roy, Purdue University, US
Abstract
Machine-learning algorithms have shown outstanding image recognition performance for computer vision applications. While these algorithms are modeled to mimic brain-like cognitive abilities, they lack the remarkable energy-efficient processing capability of the brain. Recent studies in neuroscience reveal that the brain resolves the competition among multiple visual stimuli presented simultaneously with several mechanisms of visual attention that are key to the brain's ability to perform cognition efficiently. One such mechanism known as saliency based selective attention simplifies complex visual tasks into characteristic features and then selectively activates particular areas of the brain based on the feature (or semantic) information in the input. Interestingly, we note that there is a significant similarity among underlying characteristic semantics (like color or texture) of images across multiple objects in real world applications. This presents us with an opportunity to decompose a large classification problem into simpler tasks based on semantic or feature similarity. In this paper, we propose semantic driven hierarchical learning to construct a tree-based classifier inspired by the biological visual attention mechanism for optimizing energy-efficiency of machine learning classifiers. We exploit the inherent feature similarity across images to identify the input variability and use recursive optimization procedure, to determine data partitioning at each tree node, thereby, learning the feature hierarchy. A set of binary classifiers is organized on top of the learnt hierarchy to minimize the overall test-time complexity. The feature based-learning allows selective activation of only those branches and nodes of the classification tree that are relevant to the input while keeping the remaining nodes idle. The proposed framework has been evaluated on Caltech-256 dataset and achieves 3.7x reduction in test complexity for 1.2% accuracy improvement over state-of-the-art one-vs-all tree-based method, and even higher improvements in test-time (of 5.5x) when some loss in output accuracy (up to 2.5%) is acceptable.
Download Paper (PDF; Only available from the DATE venue WiFi)

14:54

11.8.4

MACHINE LEARNING FOR RUN-TIME ENERGY OPTIMISATION IN MANY-CORE SYSTEMS
Speaker:
Rishad Shafik, Newcastle University, GB
Authors:
Dwaipayan Biswas¹, Vibishna Balagopal¹, Rishad Shafik², Bashir Al-Hashimi¹ and Geoff Merrett¹
¹University of Southampton, GB; ²Newcastle University, GB
Abstract
In recent years, the focus of computing has moved away from performance-centric serial computation to energy-efficient parallel computation. This necessitates run-time optimisation techniques to address the dynamic resource requirements of different applications on many-core architectures. In this paper, we report on intelligent run-time algorithms which have been experimentally validated for managing energy and application performance in many-core embedded system. The algorithms are underpinned by a cross-layer system approach where the hardware, system software and application layers work together to optimise the energy-performance trade-off. Algorithm development is motivated by the biological process of how a human brain (acting as an agent) interacts with the external environment (system) changing their respective states over time. This leads to a pay-off for the action taken, and the agent eventually learns to take the optimal/best decisions in future. In particular, our online approach uses a model-free reinforcement learning algorithm that suitably selects the appropriate voltage-frequency scaling based on workload prediction to meet the applications' performance requirements and achieve energy savings of up to 16% in comparison to state-of-the-art-techniques, when tested on four ARM A15 cores of an ODROID-XU3 platform.
Download Paper (PDF; Only available from the DATE venue WiFi)

15:12

11.8.5

AN EVOLUTIONARY APPROACH TO HARDWARE ENCRYPTION AND TROJAN-HORSE MITIGATION
Speaker:
Ernesto Sanchez, Politecnico di Torino, IT
Authors:
Andrea Marcelli, Marco Restifo, Ernesto Sanchez and Giovanni Squillero, Politecnico di Torino, IT
Abstract
New threats, grouped under the name of hardware attacks, became a serious concern in recent years. In a global market, untrusted parties in the supply chain may jeopardize the production of integrated circuits with intellectual-property piracy, illegal overproduction and hardware Trojan-horses (HT) injection. While one way to protect from overproduction is to encrypt the design by inserting logic gates that prevents the circuit from generating the correct outputs unless the right key is used, reducing the number of poorly-controllable signals is known to minimize the chances for an attacker to successfully hide the trigger for some malicious payload. Several approaches successfully tackled independently these two issues. This paper proposes a novel technique based on a multi-objective evolutionary algorithm able to increase hardware security by explicitly targeting both the minimization of rare signals and the maximization of the efficacy of logic encryption. Experimental results demonstrate the proposed method is effective in creating a secure encryption schema for all the circuits under test and in reducing the number rare signals on six circuits over nine, outperforming the current state of the art.
Download Paper (PDF; Only available from the DATE venue WiFi)

15:30

End of session
Coffee Break in Exhibition Area

On all conference days (Tuesday to Thursday), coffee and tea will be served during the coffee breaks at the below-mentioned times in the exhibition area.

Tuesday, March 28, 2017