Consejo Superior de Investigaciones Científicas · Universidad de Sevilla
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An efficient transformer modeling approach for mm-wave circuit design
F. Passos, E. Roca, J. Sieiro, R. Castro-Lopez and F.V. Fernandez
Journal Paper - AEU - International Journal of Electronics and Communications, vol. 128, article 153496, 2021
ELSEVIER    DOI: 10.1016/j.aeue.2020.153496    ISSN: 1434-8411    » doi
[abstract]
In this paper, a Gaussian-process surrogate modeling methodology is used to accurately and efficiently model transformers, which are still a bottleneck in radio-frequency and millimeter-wave circuit design. The proposed model is useful for a wide range of frequencies from DC up to the millimeter-wave range (over 100 GHz). The technique is statistically validated against full-wave electromagnetic simulations. The efficient model evaluation enables its exploitation in iterative user-driven design approaches, as well as automated design exploration involving thousands of simulations. As experimental results, the model is used in several scenarios, such as the design of an inter-stage amplifier operating at 60 GHz, where the model assisted in the simulation of the transformers and baluns used, and the design of individual transformers and a matching network.

Improving the reliability of SRAM-based PUFs under varying conditions
P. Sarazá-Canflanca, H. Carrasco-López, P. Brox, R. Castro-López, E. Roca and F.V. Fernández
Conference - Conference on Design of Circuits and Integrated Systems DCIS 2020
[abstract]
Abstract not available

Secure Management of IoT Devices Based on Blockchain Non-fungible Tokens and Physical Unclonable Functions
J. Arcenegui, R. Arjona and I. Baturone
Journal Paper - Lecture Notes in Computer Science, ACNS 2020: Applied Cryptography and Network Security Workshops, vol. 12418, pp 24-40, 2020
SPRINGER    DOI: 10.1007/978-3-030-61638-0_2    ISSN: 0302-9743    » doi
[abstract]
One of the most extended applications of blockchain technologies for the IoT ecosystem is the traceability of the data and operations generated and performed, respectively, by IoT devices. In this work, we propose a solution for secure management of IoT devices that participate in the blockchain with their own blockchain accounts (BCAs) so that the IoT devices themselves can sign transactions. Any blockchain participant (including IoT devices) can obtain and verify information not only about the actions or data they are taking but also about their manufacturers, managers (owners and approved), and users. Non Fungible Tokens (NFTs) based on the ERC-721 standard are proposed to manage IoT devices as unique and indivisible. The BCA of an IoT device, which is defined as an NFT attribute, is associated with the physical device since the secret seed from which the BCA is generated is not stored anywhere but a Physical Unclonable Function (PUF) inside the hardware of the device reconstructs it. The proposed solution is demonstrated and evaluated with a low-cost IoT device based on a Pycom Wipy 3.0 board, which uses the internal SRAM of the microcontroller ESP-32 as PUF. The operations it performs to reconstruct its BCA in Ethereum and to carry out transactions take a few tens of milliseconds. The smart contract programmed in Solidity and simulated in Remix requires low gas consumption.

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
M. Rahimiazghadi, C. Lammie, J.K. Eshraghian, M. Payvand, E. Donati, B. Linares-Barranco and G. Indiveri
Journal Paper - IEEE Transactions on Biomedical Circuits and Systems, first online, 2020
IEEE    DOI: 10.1109/TBCAS.2020.3036081    ISSN: 1932-4545    » doi
[abstract]
With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of the medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies ranging from emerging memristive devices, to established Field Programmable Gate Arrays (FPGAs), and mature Complementary Metal Oxide Semiconductor (CMOS) technology can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. After providing the required background, we unify the sparsely distributed research on neural network and neuromorphic hardware implementations as applied to the healthcare domain. In addition, we benchmark various hardware platforms by performing a biomedical electromyography (EMG) signal processing task and drawing comparisons among them in terms of inference delay and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that different accelerators and neuromorphic processors introduce to healthcare and biomedical domains. This paper can serve a large audience, ranging from nanoelectronics researchers to biomedical and healthcare practitioners in grasping the fundamental interplay between hardware, algorithms, and clinical adoption of these tools, as we shed light on the future of deep networks and spiking neuromorphic processing systems.

A Post-Quantum Biometric Template Protection Scheme Based on Learning Parity with Noise (LPN) Commitments
R. Arjona and I. Baturone
Journal Paper - IEEE Access, vol. 8, pp 182355-182365, 2020
IEEE    DOI: 10.1109/ACCESS.2020.3028703    ISSN: 2169-3536    » doi
[abstract]
Biometric recognition has the potential to authenticate individuals by an intrinsic link between the individual and their physical, physiological and/or behavioral characteristics. This leads a higher security level than the authentication solely based on knowledge or possession. One of the reasons why biometrics is not completely accepted is the lack of trust in the storage of biometric templates in external servers. Biometric data are sensitive data which should be protected as is contemplated in the data protection regulation of many countries. In this work, we propose the use of biometric Learning Parity With Noise (LPN) commitments as template protection scheme. To the best of our knowledge, this is the first proposal for biometric template protection based on the LPN problem (that is, the difficulty of decoding random linear codes), which offers post-quantum security. Biometric features are compared in the protected domain. Irreversibility, revocability, and unlinkability properties are satisfied as well as resistance to False Acceptance Rate (FAR), cross-matching, Stolen Token, and similarity-based attacks. A recognition accuracy with a 0% FAR is achieved, because user-specific secret keys are employed, and the False Rejection Ratio (FRR) can be adjusted depending on a threshold to preserve the accuracy of the unprotected scheme in the Stolen Token scenario. A good performance in terms of execution time, template storage and operation complexity is obtained for security levels at least of 80 bits. The proposed scheme is employed in a dual-factor authentication protocol from the literature to illustrate how it provides security using authentication and database (cloud) servers that can be malicious. The proposed LPN-based protected scheme can be applied to any biometric trait represented by binary features and any matching score based on Hamming or Jaccard distances. In particular, experimental results are included of a practical finger vein-based recognition .

Compact Macro-Cell with OR Pulse Combining for Low Power Digital-SiPM
I. Vornicu, F.N. Bandi, R. Carmona-Galan and A. Rodriguez-Vazquez
Journal Paper - IEEE Sensors Journal, vol. 20, no. 21, pp 12817 - 12826, 2020
IEEE    DOI: 10.1109/JSEN.2020.3002609    ISSN: 1530-437X    » doi
[abstract]
High-density digital-Silicon Photomultipliers call for high-performance Single Photon Avalanche Diodes (SPAD) front-ends. Power consumption and fill factor are significant concerns in this kind of sensors. This paper presents a compact and power-efficient macro-cell where several SPADs share the active recharge circuitry for increased fill factor. Integrated with 110nm technology for image sensors, the array of macro-cells has 30% fill factor. Also, following the first firing of any SPAD during the same macro-pixel dead-time, the other SPADs are disabled for power saving. Of course, subsequent triggers are lost. However, they would have been masked by the OR pulse combining scheme. Besides this event-driven disabling feature, the macro-cell includes circuitry to disable noisy devices - similar to other SiPM cells. Also, the macro-cell features control of the dead-time. This paper describes the macro-cell concept, its associated analysis and design equations. Key parameters of the design are discussed to optimize power consumption. Design scalability is contemplated as well. Experimental results proved that the power efficiency of the proposed scheme depends on the illumination power. Also, power efficiency is linked to the pulse overlapping probability. For example, power saving up to 30% is obtained with 4 sub-cells per macro-cell, when pulse overlapping is about 11% for correlated light or the pulse rate per sub-cell is about 100kHz for uncorrelated light.

Characterization and Monitoring of Titanium Bone Implants with Impedance Spectroscopy
A. Olmo, M. Hernandez, E. Chicardi and Y. Torres
Journal Paper - Sensors, vol. 20, no. 19, article 4358, 2020
MDPI    DOI: 10.3390/s20164358    ISSN: 1424-8220    » doi
[abstract]
Porous titanium is a metallic biomaterial with good properties for the clinical repair of cortical bone tissue, although the presence of pores can compromise its mechanical behavior and clinical use. It is therefore necessary to characterize the implant pore size and distribution in a suitable way. In this work, we explore the new use of electrical impedance spectroscopy for the characterization and monitoring of titanium bone implants. Electrical impedance spectroscopy has been used as a non-invasive route to characterize the volumetric porosity percentage (30%, 40%, 50% and 60%) and the range of pore size (100-200 and 355-500 mm) of porous titanium samples obtained with the space-holder technique. Impedance spectroscopy is proved to be an appropriate technique to characterize the level of porosity of the titanium samples and pore size, in an affordable and non-invasive way. The technique could also be used in smart implants to detect changes in the service life of the material, such as the appearance of fractures, the adhesion of osteoblasts and bacteria, or the formation of bone tissue.

VersaTile Convolutional Neural Network Mapping on FPGAs
A. Muñío-Gracia, J. Fernández-Berni, R. Carmona-Galán and A. Rodríguez-Vázquez
Conference - IEEE International Symposium on Circuits and Systems ISCAS 2020
[abstract]
Convolutional Neural Networks (ConvNets) are directed acyclic graphs with node transitions determined by a set of configuration parameters. In this paper, we describe a dynamically configurable hardware architecture that enables data allocation strategy adjustment according to ConvNets layer characteristics. The proposed flexible scheduling solution allows the accelerator design to be portable across various scenarios of computation and memory resources availability. For instance, FPGA block-RAM resources can be properly balanced for optimization of data distribution and minimization of off-chip memory accesses. We explore the selection of tailored scheduling policies that translate into efficient on-chip data reuse and hence lower energy consumption. The system can autonomously adapt its behavior with no need of platform reconfiguration nor user supervision. Experimental results are presented and compared with state-of-the-art accelerators.

3D-printed sensors and actuators in cell culture and tissue engineering: Framework and research challenges
P. Pérez, J.A. Serrano and A. Olmo
Journal Paper - Sensors, vol. 20, no. 19, article 5617, 2020
MDPI    DOI: 10.3390/s20195617    ISSN: 1424-8220    » doi
[abstract]
Three-dimensional printing technologies have been recently proposed to monitor cell cultures and implement cell bioreactors for different biological applications. In tissue engineering, the control of tissue formation is crucial to form tissue constructs of clinical relevance, and 3D printing technologies can also play an important role for this purpose. In this work, we study 3D-printed sensors that have been recently used in cell culture and tissue engineering applications in biological laboratories, with a special focus on the technique of electrical impedance spectroscopy. Furthermore, we study new 3D-printed actuators used for the stimulation of stem cells cultures, which is of high importance in the process of tissue formation and regenerative medicine. Key challenges and open issues, such as the use of 3D printing techniques in implantable devices for regenerative medicine, are also discussed.

Yield-aware multi-objective optimization of a MEMS accelerometer system using QMC-based methodologies
M. Pak, F.V. Fernandez and G. Dundar
Journal Paper - Microelectronics Journal, vol. 103, article 104876, 2020
ELSEVIER    DOI: 10.1016/j.mejo.2020.104876    ISSN: 0026-2692    » doi
[abstract]
This paper proposes a novel yield-aware optimization methodology that can be used for mixed-domain synthesis of robust micro-electro-mechanical systems (MEMS). The robust Pareto front optimization of a MEMS accelerometer system, which includes a capacitive MEMS sensor and an analog read-out circuitry, is realized by co-optimization of the mixed-domain system where the sensor performances are evaluated using highly accurate analytical models and the circuit level simulations are carried out by an electrical simulator. Two different approaches for yield-aware optimization have been implemented in the synthesis loop. The Quasi Monte Carlo (QMC) technique has been used to embed the variation effects into the optimization loop. The results for both two- and three-dimensional yield-aware optimization are quite promising for robust MEMS accelerometer synthesis.

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