Architecture-Level Optimization on Digital Silicon Photomultipliers for Medical Imaging
F. Bandi, V. Ilisie, I. Vornicu, R. Carmona-Galan, J.M. Benlloch and A. Rodriguez-Vazquez
Journal Paper · Sensors, vol. 22, no. 1, article 122, 2022
MDPI ISSN: 1424-8220
Silicon photomultipliers (SiPMs) are arrays of single-photon avalanche diodes (SPADs) connected in parallel. Analog silicon photomultipliers are built in custom technologies optimized for detection efficiency. Digital silicon photomultipliers are built in CMOS technology. Although CMOS SPADs are less sensitive, they can incorporate additional functionality at the sensor plane, which is required in some applications for an accurate detection in terms of energy, timestamp, and spatial location. This additional circuitry comprises active quenching and recharge circuits, pulse combining and counting logic, and a time-to-digital converter. This, together with the disconnection of defective SPADs, results in a reduction of the light-sensitive area. In addition, the pile-up of pulses, in space and in time, translates into additional efficiency losses that are inherent to digital SiPMs. The design of digital SiPMs must include some sort of optimization of the pixel architecture in order to maximize sensitivity. In this paper, we identify the most relevant variables that determine the influence of SPAD yield, fill factor loss, and spatial and temporal pile-up in the photon detection efficiency. An optimum of 8% is found for different pixel sizes. The potential benefits of molecular imaging of these optimized and small-sized pixels with independent timestamping capabilities are also analyzed.
Special Issue on Embedded Vision Architectures for Machine Learning
F. Berry, L. Maggiani and R. Carmona-Galan
Journal Paper · Journal of Signal Processing Systems for Signal Image and Video Technology, first online, 2022
SPRINGER ISSN: 1939-8018
This special issue presents different architectural compromises involved in the execution of heavy computational loads related to machine learning on CPUs, GPUs, FPGAs and ASICs, and even their impact on memory hierarchies and storage. This subject has always been a central topic at the Workshop on Architecture of Smart Cameras (WASC), a workshop especially dedicated to bring together researchers and engineers covering the all aspects of the implementation of smart cameras. It has been held in different cities, from Clermont-Ferrand in 2012 to Ghent in 2020. Smart cameras are embedded vision systems that are required to produce a semantic understanding of the scene and to generate a response. The incorporation of deep neural networks represents a significant leap in performance, but an efficient implementation is needed not to compromise the scarce resources found in embedded platforms. This special issue focuses on addressing this issue.
Visual Inference for IoT Systems: A Practical Approach
D. Velasco-Montero, J. Fernández-Berni and A. Rodríguez-Vázquez
Book · 145 p, 2022
SPRINGER ISBN: 978-3-030-90903-1
This book presents a systematic approach to the implementation of Internet of Things (IoT) devices achieving visual inference through deep neural networks. Practical aspects are covered, with a focus on providing guidelines to optimally select hardware and software components as well as network architectures according to prescribed application requirements.
The monograph includes a remarkable set of experimental results and functional procedures supporting the theoretical concepts and methodologies introduced. A case study on animal recognition based on smart camera traps is also presented and thoroughly analyzed. In this case study, different system alternatives are explored and a particular realization is completely developed.
Illustrations, numerous plots from simulations and experiments, and supporting information in the form of charts and tables make Visual Inference and IoT Systems: A Practical Approach a clear and detailed guide to the topic. It will be of interest to researchers, industrial practitioners, and graduate students in the fields of computer vision and IoT.
Trivium Stream Cipher Countermeasures Against Fault Injection Attacks and DFA
F.E. Potestad-Ordonez, E. Tena-Sanchez, J.M. Mora-Gutierrez, M. Valencia-Barrero and C.J. Jimenez-Fernandez
Journal Paper · IEEE Access, vol. 9, pp 168444-168454, 2021
IEEE ISSN: 2169-3536
Attacks on cryptocircuits are becoming increasingly sophisticated, requiring designers to include more and more countermeasures in the design to protect it against malicious attacks. Fault Injection Attacks and Differential Fault Analysis have proven to be very dangerous as they are able to retrieve the secret information contained in cryptocircuits. In this sense, Trivium cipher has been shown to be vulnerable to this type of attack. This paper presents four different fault detection schemes to protect Trivium stream cipher implementations against fault injection attacks and differential fault analysis. These countermeasures are based on the introduction of hardware redundancy and signature analysis to detect fault injections during encryption or decryption operations. This prevents the attacker from having access to the faulty key stream and performing differential fault analysis. In order to verify the correct operation and the effectiveness of the presented schemes, an experimental system of non-invasive active attacks using the clock signal in FPGA has been designed. This system allows to know the fault coverage for both multiple and single faults. In addition, the results of area consumption, frequency degradation, and fault detection latency for FPGA and ASIC implementations are presented. The results show that all proposed countermeasures are able to provide a fault coverage above 79% and one of them reaches a coverage of 99.99%. It has been tested that the number of cycles for fault detection is always lower than the number of cycles needed to apply the differential fault analysis reported in the literature for the Trivium cipher.