Use of a standard non-rad-hard digital cell library in the rad-hard design can be a cost-effective solution for space applications. In this paper we demonstrate how a standard non-rad-hard flip-flop, as one of the most vulnerable digital cells, can be converted into a rad-hard flip-flop without modifying its internal structure. We present five variants of a Triple Modular Redundancy (TMR) flip-flop: baseline TMR flip-flop, latch-based TMR flip-flop, True-Single Phase Clock (TSPC) TMR flip-flop, scannable TMR flip-flop and self-correcting TMR flip-flop. For all variants, the multi-bit upsets have been addressed by applying special placement constraints, while the Single Event Transient (SET) mitigation was achieved through the usage of customized SET filters and selection of optimal inverter sizes for the clock and reset trees. The proposed flip-flop variants feature differing performance, thus enabling to choose the optimal solution for every sensitive node in the circuit, according to the predefined design constraints. Several flip-flop designs have been validated on IHP’s 130nm BiCMOS process, by irradiation of custom-designed shift registers. It has been shown that the proposed TMR flip-flops are robust to soft errors with a threshold Linear Energy Transfer (LET) from ( $32.4\, \frac { {\mathrm { \text {M} \text {eV} }}\cdot {\mathrm { \text {c} \text {m} }}^{2}}{ {\mathrm { \text {m} \text {g} }}}$ ) to ( $62.5\, \frac { {\mathrm { \text {M} \text {eV} }}\cdot {\mathrm { \text {c} \text {m} }}^{2}}{ {\mathrm { \text {m} \text {g} }}}$ ), depending on the variant.

The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.

Biological brains are increasingly taken as a guide toward more efficient forms of computing. The latest frontier considers the use of spiking neural-network-based neuromorphic processors for near-sensor data processing, in order to fit the tight power and resource budgets of edge computing devices. However, a prevailing focus on brain-inspired computing and storage primitives in the design of neuromorphic systems is currently bringing a fundamental bottleneck to the forefront: chip-scale communications. While communication architectures (typically, a network-on-chip) are generally inspired by, or even borrowed from, general purpose computing, neuromorphic communications exhibit unique characteristics: they consist of the event-driven routing of small amounts of information to a large number of destinations within tight area and power budgets. This article aims at an inflection point in network-on-chip design for brain-inspired communications, revolving around the combination of cost-effective and robust asynchronous design, architecture specialization for short messaging and lightweight hardware support for tree-based multicast. When validated with functional spiking neural network traffic, the proposed NoC delivers energy savings ranging from 42% to 71% over a state-of-the-art NoC used in a real multi-core neuromorphic processor for edge computing applications.

Resistive random access memory (RRAM)-based hardware accelerators are playing an important role in the implementation of in-memory computing (IMC) systems for artificial intelligence applications. The latter heavily rely on vector-matrix multiplication (VMM) operations that can be efficiently boosted by RRAM devices. However, the stochastic nature of the RRAM technology is still challenging real hardware implementations. To study the accuracy degradation of consecutive VMM operations, in this work we programed two RRAM subarrays composed of $8\times $ 8 one-transistor-one-resistor (1T1R) cells following two different distributions of conductive levels. We analyze their robustness against 1000 identical consecutive VMM operations and monitor the inherent devices’ nonidealities along the test. We finally quantize the accuracy loss of the operations in the digital domain and consider the trade-offs between linearly distributing the resistive states of the RRAM cells and their robustness against nonidealities for future implementation of IMC hardware systems.

In this article we propose a complete solution for the so-called inner receiver of an OFDM-WLAN system based on the IEEE 802.11a standard. We concentrate our investigations on three key components forming the inner receiver namely, the synchronizer, the channel estimator and the digital timing loop. The main goal is the joint optimization of the signal processing algorithms along with the implementation friendly VLSI architecture required for these three key components in order to reduce power, area and latency, without compromising the performance excessively. We provide both the mathematical details and extensive computer simulations to validate our design

Simulation-based fault injection is a widely used technique for early-stage circuit reliability analysis. However, it consumes significant time, particularly for complex circuits. This paper introduces two Machine Learning (ML) methodologies to predict simulation-based fault injection outcomes at the gate level. The initial approach employs Neural Networks (NNs), extracting structural features from synthesis reports and simulation-related characteristics from Value Change Dump (VCD) waveforms. Nevertheless, NNs are restricted to learning from individual gate attributes. To exploit the comprehensive structure of entire circuits, we propose a method to convert circuits into graphs. This facilitates the utilization of Graph Neural Networks (GNNs) as advanced models, resulting in improved prediction performance. We select six open-source circuits with diverse complexities and functions to validate these methodologies and explore their adaptability across various circuits. Our experiments demonstrate the superior performance of GNNs compared to NNs in terms of prediction accuracy, efficiency in hyperparameter search, and the ability to address imbalanced datasets. Additionally, we investigate the feasibility of deploying the trained models to predict results in new circuits. Based on the experimental outcomes, we present an approach for leveraging the proposed methodology to accelerate simulation-based fault injection.