Spiking Neural Networks (SNNs) are Artificial Neural Networks which promise to mimic the biological brain processing with unsupervised online learning capability for various cognitive tasks. However, SNN hardware implementation with online learning support is not trivial and might prove highly inefficient. This paper proposes an energy-efficient hardware implementation for SNN synapses. The implementation is based on parallel-connected Magnetic Tunnel Junction (MTJ) devices and exploits their inherent stochasticity. In addition, it uses a dedicated unsupervised learning rule based on optimized Spike-Timing-Dependent Plasticity (STDP). To facilitate the design of the SNN, its training and evaluation, an open-source Python-based platform is developed; it takes as input the SNN parameters and discrete circuit components, and it automatically generates the associated full netlist in SPICE; moreover, it extracts the simulation results and makes them available in python for evaluation and manipulation. Unlike conventional neuromorphic hardware that relies on simple weight mapping post-off-line training, our approach emphasizes continuous, unsupervised learning, ensuring an energy efficiency of 11.2nW per synaptic update during training and as low as 109fJ/spike during inference.@en

The research and prototyping of new memory technologies are getting a lot of attention in order to enable new (computer) architectures and provide new opportunities for today’s and future applications. Delivering high quality and reliability products was and will remain a crucial step in the introduction of new technologies. Therefore, appropriate fault modelling, test development and design for testability (DfT) is needed. This paper overviews and discusses the challenges and the emerging solutions in testing three classes of memories: 3D stacked memories, Resistive memories and Spin-Transfer-Torque Magnetic memories. Defects mechanisms, fault models, and emerging test solutions will be discussed.@en

Today's computer architectures and semiconductor technologies are facing major challenges making them incapable to deliver the required features (such as computer efficiency) for emerging applications. Alternative architectures are being under investigation in order to continue deliver sustainable benefits for the foreseeable future society at affordable cost. These architectures are not only changing the traditional computing paradigm (e.g., in terms of programming models, compilers, circuit design), but also setting up new challenges and directions on the way these architectures should be tested to guarantee the required quality and reliability levels. This paper highlights the major open questions regarding test and reliability of three emerging computing paradigms being approximate computing, computation-in-memory and neuromorphic computing.@en

The memristor is an emerging technology which is triggering intense interdisciplinary activity. It has the potential of providing many benefits, such as energy efficiency, density, reconfigurability, nonvolatile memory, novel computational structures and approaches, massive parallelism, etc. These characteristics may lead to deeply revise existing computing and storage paradigms. This paper presents a comprehensive overview of memristor technology and its potential to design a new computational paradigm.@en

The power and reliability issues of today's memories (static and dynamic RAMs) reduce the advances achieved by their implementation in scaled technology. There are several emergent memory technologies that address the technical constraints of today's memories, among which the most promising solutions are the resistance-based memories, such as phase change memories, the redox-based resistive memories, and the spin-transfer torque magnetic memories. These technologies are facing various challenges that have to be addressed to render them efficient in today's applications. Until recently, research focus was on the design for performance and power efficiency, but lately, the test and reliability issues of these devices have become of major concern to the community. This paper presents an overview of the challenges and proposed test and reliability boost solutions developed to suit the needs of the emerging memories under analysis. It underlines (1) the unique faults that occur in the memory cell due to known issues in the emerging storage devices, (2) the dedicated solutions developed for efficient testing of the emerging memories under study, (3) the main reliability concerns related to the emerging storage devices, and (4) the design solutions targeted at mitigating these reliability issues.@en