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DTSTART;TZID=America/Los_Angeles:20200206T093000
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DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
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SUMMARY:Efficient Offline and Online Training of Memristive Neuromorphic Hardware
DESCRIPTION:Name: Mohammed Fouda\nChair: Prof. Ahmed Eltawil \nDate: February 6th\, 2020 \nTime: 9:30 AM \nLocation:  EH 3206 \nCommittee: Ahmed Eltawil (Chair)\, Prof. Fadi Kurdahi\, Prof. Nikil Dutt\, Prof. Emre Neftci \nTitle: “Efficient Offline and Online Training of Memristive Neuromorphic Hardware” \nAbstract: \nBrain-inspired neuromorphic systems have witnessed rapid development over the last decade from both algorithmic and hardware perspectives. Neuromorphic hardware promises to be more energy- and speed- efficient as compared to traditional Von-Neumann architectures. Thanks to the recent progress in solid-state devices\, different nanoscale-nonvolatile memory devices\, such as RRAMs (memristors)\, STT-RAM and PCM\, support computations based on mimicking biological synaptic response. The most important advantage of these devices is their ability to be sandwiched between interconnect wires creating crossbar array structures that are inherently able to perform matrix-vector multiplication (MVM) in one step. Despite the great potential of RRAMs\, they suffer from numerous nonidealities limiting the performance\, including\, high variability\, asymmetric and nonlinear weight update\, endurance\, retention and stuck at fault (SAF) defects in addition to the interconnect wire resistance that creates sneak paths. This thesis will focus on the application of RRAMs for neuromorphic computation while accounting for the impact of device nonidealities on neuromorphic hardware. \n\n\n\n\nIn this thesis\, we propose software-level solutions to mitigate the impact of nonidealities\, that highly affect the offline (ex-situ) training\, without incorporating expensive SPICE or numerical simulations. We propose two techniques to incorporate the effect of sneak path problem during training\, in addition to the device’s variability\, with negligible overhead. The first technique is inspired by the impact of the sneak path problem on the stored weights (devices’ conductances) which we referred to as the mask technique. This mask is element-wise multiplied by the weights during the training. This mask can be obtained from measured weights of fabricated hardware. The other solution is a neural network estimator which is trained by our SPICE-like simulator. The test validation results\, done through our SPICE-like framework\, show significant improvement in performance\, close to the baseline BNNs and QNNs\, which demonstrates the efficiency of the proposed methods. Both techniques show the high ability to capture the problem for multilayer perceptron networks for MNIST dataset with negligible runtime overhead. In addition\, the proposed neural estimator outperforms the mask technique for challenging datasets such as CIFAR10. Furthermore\, other nonidealities such as SAF defects and retention are evaluated. \nWe also develop a model to incorporate the stochastic asymmetric nonlinear weight update in online (in-situ) training. We propose two solutions for this problem; 1) a compensation technique which is tested on a small scale problem to separate two Laplacian mixed sources using online independent component analysis. 2) stochastic rounding and is tested on a spiking neural network with deep local learning dynamics showing only a 1~2% drop in the baseline accuracy for three different RRAM devices. We also propose Error-triggered learning to overcome the limited endurance problem with only 0.3% and 3% drop in the accuracy for N-MNIST and DVSGesture datasets with around 33X and 100X reduction in the number of writes\, respectively. \nFinally\, the prospects of this neuromorphic hardware are discussed to develop new algorithms with the existing resistive crossbar hardware including its nonidealities.
URL:https://www.cecs.uci.edu/event/efficient-offline-and-online-training-of-memristive-neuromorphic-hardware/
CATEGORIES:PhD Defenses
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DTSTART;TZID=America/Los_Angeles:20200220T100000
DTEND;TZID=America/Los_Angeles:20200220T110000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14577-1582192800-1582196400@www.cecs.uci.edu
SUMMARY:Novel Monitoring\, Detection\, and Optimization Techniques for Neurological Disorders
DESCRIPTION:Name: Seyede Mahya Safavi\nChair: Prof. Pai Chou \nDate: February 20th\, 2020 \nTime: 10:00 AM \nLocation:  EH 3404 \nCommittee: Prof. Pai Chou (Chair)\, Prof. Beth Lopour\, Prof. Phillip Sheu \nTitle: “Novel Monitoring\, Detection\, and Optimization Techniques for Neurological Disorders” \nAbstract: \nThe advancement in chronically implanted neural recording devices has led to advent of assisting devices for rehabilitation and restoring lost sensorimotor functions in patients suffering from paralysis. Electrocorticogram (ECoG) signal can record high-Gamma sub-band activity known to be related to hand movements. In the first part of this work\, we Propose a finger movement detection technique based on ECoG source localization. In fact\, the finger flexion and extension are originating in slightly different areas of motor cortex. The origin of the brain activity is used as the distinctive feature for decoding the finger movement. \nThe real-time implementation of brain source localization is challenging due to extensive iterations in the existing solutions. In the second part of this work\, we have proposed two techniques to reduce the computational complexity of the Multiple Signal Classification (MUSIC) algorithm. First the cortex surface is parsed into several regions. Next\, a novel nominating procedure will pick a number of regions to be searched for brain activity. In the second step\, an electrode selection technique based on the Cramer-Rao bound of the errors is proposed to select the best set of an arbitrary number of electrodes. The proposed techniques lead to 90% reduction in computational complexity while maintaining a good concordance in terms of localization error compared to regular MUSIC algorithm. \nEpilepsy is a neurological disorder with multiple comorbid conditions including cardiovascular and respiratory disorders. The cardiovascular imbalance is of great importance since the mechanisms of Sudden Unexpected Death in Epilepsy (SUDEP) is still unknown. The ictal tachycardia is the most well-known cardiac imbalance during the seizure. In the third part of this dissertation\, we used an optical sensor called photoplethysmogram (PPG) to investigate the variations in ictal blood flow in limbs. Six different features related to hemodynamics were derived from PPG pulse morphology. A consistent pattern of ictal change was observed across all the subjects/seizures. These variations suggest an increase in vascular resistance due to an increase in sympathetic tone. The timing analysis of the PPG features revealed some PPG feature variations can precede the ictal tachycardia by 50 seconds. These features were used to train a neural network based on Long Short Term Memory LSTM architecture for automatic seizure detection. We were able to reduce the False Alarm rate by 50% compared to other heart rate variability based detectors.
URL:https://www.cecs.uci.edu/event/novel-monitoring-detection-and-optimization-techniques-for-neurological-disorders/
CATEGORIES:PhD Defenses
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