In complex embedded applications, optimisation and adaptation of both dynamic and leakage power have become an issue at SoC grain. A fully power-aware globally-asynchronous locally-synchronous network-on-chip (NoC) circuit is presented in this paper. Network-on-chip architecture combined with a globally-asynchronous locally-synchronous paradigm is a natural enabler for DVFS mechanisms. The circuit is arranged around an asynchronous network-on-chip providing scalable communication and a 17 Gb/s throughput while automatically reducing its power consumption by activity detection. Both dynamic and static power consumptions are globally reduced using adaptive design techniques applied locally for each synchronous NoC units. No fine control software is required during voltage and frequency scaling. Power control is localized and a minimal latency cost is observed.

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.

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.

The design of cryptographic engines for the Internet of Things (IoT) edge devices and other ultralightweight devices is a crucial challenge. The emergence of such resource-constrained devices raises significant challenges to current cryptographic algorithms. PHOTON is an ultra-lightweight cryptographic hash function targeting low-resource devices. The currently implemented hardware architectures of PHOTON hash function utilize a large amount of resources and have low operating frequencies with a low rate of throughputs. Maximum operating frequency and throughput of PHOTON architecture can be improved but at the cost of larger area utilization. Therefore, to improve the area-performance trade-offs of PHOTON hash function, an iterative architecture is implemented in this work. The concern is with the most lightweight version of PHOTON hash function with the hash size of 80 bits. It is implemented and verified on several Xilinx and Altera Field Programmable Gate Array (FPGA) devices using their synthesis and simulation tools. Low-cost and high-processing FPGA devices were both considered. The design is optimized for performance, whereas the area utilization is also taken into consideration. The overall performance and logic utilization are benchmarked with the existing implementations. The results show an improvement rate of 10.26% to 51.04% in the speed performance and a reduction rate of 7.55% to 60.64% in area utilization compared to existing implementations of PHOTON hash functions.

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 ×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 ×4 and ×5 higher. In addition, the response time is kept below 65000 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.

In this paper, we propose a novel AES microarchitecture with 32-bit datapath optimized for low-power and low-energy consumption targeting IoT applications. The proposed design uses simple shift registers for key/data storage and permutation to minimize the area, and the power/energy consumption. These shift registers also minimize the control logics in the key expansion and the encryption path. The proposed architecture is further optimized for area and/or power/energy consumption by selecting a suitable implementation of S-boxes and applying the clock gating technique. The implementation results in TSMC 65nm technology show that our design can save 20% of area or 20% of energy per bit at the same area when compared with the current 32-bit datapath designs. Our design also occupies smaller core area with lower energy per bit and at least 4 times higher in throughput in comparison with other 8-bit designs in the same technology node.