No abstract

Manufacturing defects can cause faults in FinFET SRAMs. Of them, easy-to-detect (ETD) faults always cause incorrect behavior, and therefore are easily detected by applying sequences of write and read operations. However, hard-to-detect (HTD) faults may not cause incorrect behavior, only parametric deviations. Detection of these faults is of major importance as they may lead to test escapes. This paper proposes a new designfor-testability (DFT) scheme for FinFET SRAMs to detect such faults by creating a mismatch in the sense amplifier (SA). This mismatch, combined with the defect in the cell, will incorrectly bias the SA and cause incorrect read outputs. Furthermore, postsilicon calibration schemes can be used to avoid over-testing or test escapes caused by process variation effects. Compared to the state of the art, this scheme introduces negligible overheads in area and test time while it significantly improves fault coverage and reduces the number of test escapes.

This paper presents a solution to the test time minimization problem for core-based systems. We assume a hybrid BIST approach, where a test set is assembled, for each core, from pseudorandom test patterns that are generated online, and deterministic test patterns that are generated off-line and stored in the system. In this paper we propose an iterative algorithm to find the optimal combination of pseudorandom and deterministic test sets of the whole system, consisting of multiple cores, under given memory constraints, so that the total test time is minimized. Our approach employs a fast estimation methodology in order to avoid exhaustive search and to speed-up the calculation process. Experimental results have shown the efficiency of the algorithm to find a near optimal solutions.

This study describes the Computing Platforms (CPs) and the hardware reliability issues of Unmanned Aerial Vehicles (UAVs), or drones, which recently attracted significant attention in mission and safety-critical applications demanding a failure-free operation. While the rapid development of the UAV technologies was recently reviewed by survey reports focusing on the architecture, cost, energy efficiency, communication, and civil application aspects, the computing platforms’ reliability perspective was overlooked. Moreover, due to the rising complexity and diversity of today’s UAV CPs, their reliability is becoming a prominent issue demanding up-to-date solutions tailored to the UAV specifics. The objective of this work is to address this gap, focusing on the hardware reliability aspect. This research studies the UAV CPs deployed for representative applications, specific fault and failure modes, and existing approaches for reliability assessment and enhancement in CPs for failure-free UAV operation. This study indicates how faults and failures occur in the various system layers of UAVs and analyzes open challenges. We advocate a concept of a cross-layer reliability model tailored to UAVs’ onboard intelligence and identify directions for future research in this area.

Today, unmanned aerial vehicles (UAVs) or drones are increasingly used to enable and support multi-access edge computing (MEC). However, transferring data between nodes in such dynamic networks implies considerable latency and energy consumption, which are significant issues for practical real-time applications. In this paper, we consider an autonomous swarm of heterogeneous drones. This is a general architecture that can be applied for applications that need in-field computation, e.g. real-time object detection in video streams. Collaborative computing in a swarm of drones has the potential to improve resource utilization in a real-time application i.e., each drone can execute computations locally or offload them to other drones. In such an approach, drones need to compete for using each other's resources; therefore, efficient orchestration of the communication and offloading at the swarm level is essential. The main problem investigated in this work is computation offloading between drones in a swarm. To tackle this problem, we propose a novel federated learning (FL)-based fast and fair offloading strategy with a rating method. The simulation results demonstrate the effectiveness of the proposed strategy over other existing methods and architectures with average improvements of -23% in energy consumption, -15% in latency, +18% in throughput, and +9% in fairness.INDEX TERMS Swarm of drones, Multi-access edge computing, Collaborative computing, Federated learning.