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While general-purpose computing follows Von Neumann's architecture, the data movement between memory and processor elements dictates the processor's performance. The evolving compute-in-memory (CiM) paradigm tackles this issue by facilitating simultaneous processing and storage within static random-access memory (SRAM) elements. Numerous design decisions taken at different levels of hierarchy affect the figure of merits (FoMs) of SRAM, such as power, performance, area, and yield. The absence of a rapid assessment mechanism for the impact of changes at different hierarchy levels on global FoMs poses a challenge to accurately evaluating innovative SRAM designs. This paper presents an automation tool designed to optimize the energy and latency of SRAM designs incorporating diverse implementation strategies for executing logic operations within the SRAM. The tool structure allows easy comparison across different array topologies and various design strategies to result in energy-efficient implementations. Our study involves a comprehensive comparison of over 6900+ distinct design implementation strategies for EPFL combinational benchmark circuits on the energy-recycling resonant compute-in-memory (rCiM) architecture designed using TSMC 28 nm technology. When provided with a combinational circuit, the tool aims to generate an energy-efficient implementation strategy tailored to the specified input memory and latency constraints. The tool reduces 80.9% of energy consumption on average across all benchmarks while using the six-topology implementation compared to baseline implementation of single-macro topology by considering the parallel processing capability of rCiM cache size ranging from 4KB to 192KB.

Smart focal-plane and in-chip image processing has emerged as a crucial technology for vision-enabled embedded systems with energy efficiency and privacy. However, the lack of special datasets providing examples of the data that these neuromorphic sensors compute to convey visual information has hindered the adoption of these promising technologies. Neuromorphic imager variants, including event-based sensors, produce various representations such as streams of pixel addresses representing time and locations of intensity changes in the focal plane, temporal-difference data, data sifted/thresholded by temporal differences, image data after applying spatial transformations, optical flow data, and/or statistical representations. To address the critical barrier to entry, we provide an annotated, temporal-threshold-based vision dataset specifically designed for face detection tasks derived from the same videos used for Aff-Wild2. By offering multiple threshold levels (e.g., 4, 8, 12, and 16), this dataset allows for comprehensive evaluation and optimization of state-of-the-art neural architectures under varying conditions and settings compared to traditional methods. The accompanying tool flow for generating event data from raw videos further enhances accessibility and usability. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds, enabling more accurate and efficient object detection and localization and ultimately promoting the broader adoption of low-power, neuromorphic imaging technologies. To support further research, we publicly released the dataset at \url{https://dx.doi.org/10.21227/bw2e-dj78}., Comment: 8 pages

Medical imaging informatics is a rapidly growing field that combines the principles of medical imaging and informatics to improve the acquisition, management, and interpretation of medical images. This chapter introduces the basic concepts of medical imaging informatics, including image processing, feature engineering, and machine learning. It also discusses the recent advancements in computer vision and deep learning technologies and how they are used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care., Comment: 18 pages, 11 figures, 2 tables; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging"

As the demand for high-performance microprocessors increases, the circuit complexity and the rate of data transfer increases resulting in higher power consumption. We propose a clocking architecture that uses a series LC resonance and inductor matching technique to address this bottleneck. By employing pulsed resonance, the switching power dissipated is recycled back. The inductor matching technique aids in reducing the skew, increasing the robustness of the clock network. This new resonant architecture saves over 43% power and 91% skew clocking a range of 1--5 GHz, compared to a conventional primary-secondary flip-flop-based CMOS architecture.

The national highway traffic safety administration (NHTSA) identified cybersecurity of the automobile systems are more critical than the security of other information systems. Researchers already demonstrated remote attacks on critical vehicular electronic control units (ECUs) using controller area network (CAN). Besides, existing intrusion detection systems (IDSs) often propose to tackle a specific type of attack, which may leave a system vulnerable to numerous other types of attacks. A generalizable IDS that can identify a wide range of attacks within the shortest possible time has more practical value than attack-specific IDSs, which is not a trivial task to accomplish. In this paper we propose a novel {\textbf g}raph-based {\textbf G}aussian {\textbf n}aive {\textbf B}ayes (GGNB) intrusion detection algorithm by leveraging graph properties and PageRank-related features. The GGNB on the real rawCAN data set~\cite{Lee:2017} yields 99.61\%, 99.83\%, 96.79\%, and 96.20\% detection accuracy for denial of service (DoS), fuzzy, spoofing, replay, mixed attacks, respectively. Also, using OpelAstra data set~\cite{Guillaume:2019}, the proposed methodology has 100\%, 99.85\%, 99.92\%, 100\%, 99.92\%, 97.75\% and 99.57\% detection accuracy considering DoS, diagnostic, fuzzing CAN ID, fuzzing payload, replay, suspension, and mixed attacks, respectively. The GGNB-based methodology requires about $239\times$ and $135\times$ lower training and tests times, respectively, compared to the SVM classifier used in the same application. Using Xilinx Zybo Z7 field-programmable gate array (FPGA) board, the proposed GGNB requires $5.7 \times$, $5.9 \times$, $5.1 \times$, and $3.6 \times$ fewer slices, LUTs, flip-flops, and DSP units, respectively, than conventional NN architecture.

Although we may be at the end of Moore's law, lowering chip power consumption is still the primary driving force for the designers. To enable low-power operation, we propose a resonant energy recovery static random access memory (SRAM). We propose the first series resonance scheme to reduce the dynamic power consumption of the SRAM operation. Besides, we identified the requirement of supply boosting of the write buffers for proper resonant operation. We evaluated the resonant 144KB SRAM cache through SPICE and test chip using a commercial 28nm CMOS technology. The experimental results show that the resonant SRAM can save up to 30% dynamic power at 1GHz operating frequency compared to the state-of-the-art design., Comment: This paper is accepted to IEEE Transactions on Circuits and Systems--II

The controller area network (CAN) is the most widely used intra-vehicular communication network in the automotive industry. Because of its simplicity in design, it lacks most of the requirements needed for a security-proven communication protocol. However, a safe and secured environment is imperative for autonomous as well as connected vehicles. Therefore CAN security is considered one of the important topics in the automotive research community. In this paper, we propose a four-stage intrusion detection system that uses the chi-squared method and can detect any kind of strong and weak cyber attacks in a CAN. This work is the first-ever graph-based defense system proposed for the CAN. Our experimental results show that we have a very low 5.26% misclassification for denial of service (DoS) attack, 10% misclassification for fuzzy attack, 4.76% misclassification for replay attack, and no misclassification for spoofing attack. In addition, the proposed methodology exhibits up to 13.73% better accuracy compared to existing ID sequence-based methods., Comment: This paper is accepted to IEEE Transactions on Intelligent Transportation Systems for publication