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IoT devices and embedded systems are deployed in critical environments, emphasizing attributes like power efficiency and computational capabilities. However, these constraints stress the paramount importance of device security, stimulating the exploration of lightweight cryptographic mechanisms. This study introduces a lightweight architecture for authenticated encryption tailored to these requirements. The architecture combines the lightweight encryption of the LED block cipher with the authentication of the PHOTON hash function. Leveraging shared internal operations, the integration of these bases optimizes area–performance tradeoffs, resulting in reduced power consumption and a reduced logic footprint. The architecture is synthesized and simulated using Verilog HDL, Quartus II, and ModelSim, and implemented on Cyclone FPGA devices. The results demonstrate a substantial 14% reduction in the logic area and up to a 46.04% decrease in power consumption in contrast to the individual designs of LED and PHOTON. This work highlights the potential for using efficient cryptographic solutions in resource-constrained environments. [ABSTRACT FROM AUTHOR]

For most Internet-of-Things (IoT) applications, embedded processors typically execute lightweight tasks such as sensing and communication. The typical IoT program senses some information and sends them via a channel, usually a wireless channel with an RF circuit. These IoT nodes often require a system with networking capabilities and a low-power harvester implementation. This brief presents a sub-μW 8-bit processor which is suitable for such IoT applications. The processor implements the Open8 Instruction Set Architecture (ISA) with an 8-bit datapath and 16-bit bus addressing. The chip contains the processor and a 4-KB of Static Random-Access-Memory (SRAM), and is fabricated by the 65-nm Silicon-On-Thin-Box (SOTB) process. The SOTB process is one of the Fully-Depleted Silicon-On-Insulator (FD-SOI) technology. Hence, the ability to control biasing voltages is one of its key advantages to achieve low-power. The experimental results show that the power consumption at the reverse-body bias can reach down to 50-nW with 0.5-V supply voltage and 32-KHz operating clock frequency. The completed microcontroller consists of the Open8 processor, 32-KB of Read-Only-Memory (ROM), 4-KB of SRAM, Serial Peripheral Interface (SPI), SPI programmer, debug module, General-Purpose In-Outs (GPIOs), and UART. The system was tested using an XC7A100T Xilinx Field-Programmable Gate Array (FPGA); it yielded 1.8% of the total FPGA utilization. [ABSTRACT FROM AUTHOR]

The authors present a novel wideband two‐dimensional voltage‐controlled attenuator (VCA). The proposed design employs both stacked‐FET configuration and distributed structure to achieve wideband performance, high power, and high dynamic range simultaneously. A systematic design methodology is also illustrated along with a fabricated prototype to verify the concept. The chip fabricated in a 0.15‐μm Gallium Arsenide (GaAs) process exhibiting a measured power at 1‐dB compression (P1dB) of 25.5 dBm with a corresponding dynamic range of 32 dB. The insertion loss ranges from 2 to 5 dB over the bandwidth from 2 to 40 GHz. Furthermore, the chip is not only very compact (0.84 mm2), but it also requires only a single positive supply voltage, which makes it more appealing to highly integrated wireless applications. [ABSTRACT FROM AUTHOR]

In order to detect and localize through-silicon-via (TSV) failures in both manufacturing and operating phases, most of the existing methods use a dedicated testing mechanism with long response time and prerequisite interruptions for online testing. This article presents an error correction code (ECC)-based method named “TSV on-communication test” (TSV-OCT) to detect and localize faults without halting the operation of TSV-based 3-D-IC systems. We first propose a statistical detector, a method to detect open and short defects in TSVs that work in parallel with data transactions. Second, we propose an isolation-and-check algorithm to enhance the localization ability of the method. Moreover, the Monte Carlo simulations show that the proposed statistical detector increases $\times 2$ the number of detected faults when compared to conventional ECC-based techniques. With the help of isolation and check, TSV-OCT localizes the number of defects up to $\times 4$ and $\times 5$ higher. In addition, the response time is kept below 65 000 cycles, which could be easily integrated into real-time applications. On the other hand, an implementation of TSV-OCT on a 3-D Network-on-Chip (NoC) router shows no performance degradation for testing while having a reasonable area overhead. [ABSTRACT FROM AUTHOR]

Connected devices are getting attention because of the lack of security mechanisms in current Internet-of-Thing (IoT) products. The security can be enhanced by using standardized and proven-secure block ciphers as advanced encryption standard (AES) for data encryption and authentication. However, these security functions take a large amount of processing power and power/energy consumption. In this paper, we present our hardware optimization strategies for AES for high-speed ultralow-power ultralow-energy IoT applications with multiple levels of security. Our design supports multiple security levels through different key sizes, power and energy optimization for both datapath and key expansion. The estimated power results show that our implementation may achieve an energy per bit comparable with the lightweight standardized algorithm PRESENT of less than 1 pJ/b at 10 MHz at 0.6 V with throughput of 28 Mb/s in ST FDSOI 28-nm technology. In terms of security evaluation, our proposed datapath, 32-b key out of 128 b cannot be revealed by correlation power analysis attack using less than 20 000 traces. [ABSTRACT FROM AUTHOR]

The component’s failure in network-on-chips (NoCs) has been a critical factor on the system’s reliability. In order to alleviate the impact of faults, fault tolerance has been investigated in the recent years to enhance NoC’s robustness. Due to the vast selection of fault-tolerance mechanisms and critical design constraints, selecting and configuring an appropriate mechanism to satisfy the fault-tolerance requirements constitute new challenges for designers. Consequently, reliability assessment has become prominent for the early stages of manufacturing process to solve these problems. This paper approaches the fault-tolerance analysis by providing an analytical model to approximate the lifetime reliability and compares it with a system-level simulation. Based on the proposed approach, we measure the fault-tolerance efficiency using a new parameter, named reliability acceleration factor. The goal of this paper is to provide an efficient and accurate reliability assessment to help designers easily understand and evaluate the advantages and drawbacks of their potential fault-tolerance methods. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms. [ABSTRACT FROM AUTHOR]

HEVC-standardized encoders employ the CABAC (context-based adaptive binary arithmetic coding) to achieve high compression ratios and video quality that supports modern real-time high-quality video services. Binarizer is one of three main blocks in a CABAC architecture, where binary symbols (bins) are generated to feed the binary arithmetic encoder (BAE). The residual video data occupied an average of 75% of the CABAC's work-load, thus its performance will significantly contribute to the overall performance of whole CABAC design. This paper proposes an efficient hardware implementation of a binarizer for CABAC that focuses on low area cost, low power consumption while still providing enough bins for high-throughput CABAC. On the average, the proposed design can process upto 3.5 residual syntax elements (SEs) per clock cycle at the maximum frequency of 500 MHz with an area cost of 9.45 Kgates (6.41 Kgates for the binarizer core) and power consumption of 0.239 mW (0.184 mW for the binarizer core) with NanGate 45 nm technology. It shows that our proposal achieved a high overhead-efficiency of 1.293 Mbins/Kgate/mW, much better than the other related high performance designs. In addition, our design also achieved a high power-efficiency of 8288 Mbins/mW; this is important factor for handheld applications. [ABSTRACT FROM AUTHOR]