New Design Techniques for Dynamic Reconfigurable Architectures

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Autonomously Reconfigurable Artificial Neural Network on a Chip

Artificial neural network (ANN), an established bio-inspired computing paradigm, has proved very effective in a variety of real-world problems and particularly useful for various emerging biomedical applications using specialized ANN hardware. Unfortunately, these ANN-based systems are increasingly vulnerable to both transient and permanent faults due to unrelenting advances in CMOS technology scaling, which sometimes can be catastrophic. The considerable resource and energy consumption and the lack of dynamic adaptability make conventional fault-tolerant techniques unsuitable for future portable medical solutions. Inspired by the self-healing and self-recovery mechanisms of human nervous system, this research seeks to address reliability issues of ANN-based hardware by proposing an Autonomously Reconfigurable Artificial Neural Network (ARANN) architectural framework. Leveraging the homogeneous structural characteristics of neural networks, ARANN is capable of adapting its structures and operations, both algorithmically and microarchitecturally, to react to unexpected neuron failures. Specifically, we propose three key techniques --- Distributed ANN, Decoupled Virtual-to-Physical Neuron Mapping, and Dual-Layer Synchronization --- to achieve cost-effective structural adaptation and ensure accurate system recovery. Moreover, an ARANN-enabled self-optimizing workflow is presented to adaptively explore a "Pareto-optimal" neural network structure for a given application, on the fly. Implemented and demonstrated on a Virtex-5 FPGA, ARANN can cover and adapt 93% chip area (neurons) with less than 1% chip overhead and O(n) reconfiguration latency. A detailed performance analysis has been completed based on various recovery scenarios

Identification of prognostic biomarkers of cortical stroke in mouse model

According to the World Health Organization (WHO), worldwide, 15 million people suffer stroke each year. Stroke, i.e. the sudden and severe reduction of blood flow to a brain region, is the second leading cause of death and the third leading cause of disability. Stroke is also a leading cause of dementia and depression. A focal brain damage inevitably causes a drastic alteration of the whole complex neural network that characterizes the affected area. Although stroke damage can be devastating, many patients survive the initial event and display a spontaneous recovery, which can be further increased by rehabilitation therapy. Recovery is possible due to a reorganization of spared areas and connections, i.e. neuroplasticity. Functional recovery is highly variable in stroke patients and strongly depends on many factors (lesion location and volume, etc.). Currently, there are no ways of predicting either the degree or time course of recovery in individual subjects. For these reasons, the identification of biomarkers is crucial in the design and interpretation of stroke rehabilitation trials. Therefore, the aim of this work is the development of new prognostic and therapeutic tools in preclinical models. In this study I exploit a mouse model of stroke, the Middle cerebral artery occlusion (MCAO), that shows a higher variability and is thus closer to the human condition. I conducted experiments to evaluate the occurrence of motor deficits using a battery of behavioral tasks: gridwalk test, skilled reaching test, and retraction task in the M-platform (a robotic device that permits to quantitatively evaluate several kinetic/kinematic parameters related to forelimb movement). Moreover, the ischaemic lesion and electrophysiological alterations was analysed by means of histology and electroencephalographic signals (EEG) respectively. I studied how these mechanisms are altered by stroke, combining the data all these parameters, in order to define possible biomarkers that predict long-term motor recovery. The results obtained, permit new opportunities for therapeutic approaches after stroke allowing the definition of more effective rehabilitation paradigms that can be translated into clinical practice