Electronic neuroprocessors

The JPL Center for Space Microelectronics Technology (CSMT) is actively pursuing research in the neural network theory, algorithms, and electronics as well as optoelectronic neural net hardware implementations, to explore the strengths and application potential for a variety of NASA, DoD, as well as commercial application problems, where conventional computing techniques are extremely time-consuming, cumbersome, or simply non-existent. An overview of the JPL electronic neural network hardware development activities and some of the striking applications of the JPL electronic neuroprocessors are presented

A decade of neural networks: Practical applications and prospects

On May 11-13, 1994, JPL's Center for Space Microelectronics Technology (CSMT) hosted a neural network workshop entitled, 'A Decade of Neural Networks: Practical Applications and Prospects,' sponsored by DOD and NASA. The past ten years of renewed activity in neural network research has brought the technology to a crossroads regarding the overall scope of its future practical applicability. The purpose of the workshop was to bring together the sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and development prospects, with emphasis on practical applications. Of the 93 participants, roughly 15% were from government agencies, 30% were from industry, 20% were from universities, and 35% were from Federally Funded Research and Development Centers (FFRDC's)

Predictability in space launch vehicle anomaly detection using intelligent neuro-fuzzy systems

Included in this viewgraph presentation on intelligent neuroprocessors for launch vehicle health management systems (HMS) are the following: where the flight failures have been in launch vehicles; cumulative delay time; breakdown of operations hours; failure of Mars Probe; vehicle health management (VHM) cost optimizing curve; target HMS-STS auxiliary power unit location; APU monitoring and diagnosis; and integration of neural networks and fuzzy logic

Robust, Interpretable, and Portable Deep Learning Systems for Detection of Ophthalmic Diseases

The World Health Organization estimates that there are 285 million people suffering from visual impairment worldwide. The top two causes of uncorrectable vision loss are glaucoma and age-related macular degeneration (AMD), with 112 million people anticipated to be impacted by glaucoma by 2040 and nearly 15% of U.S. adults aged 43-86 predicted to be diagnosed with AMD over the next 15 years. To slow the progression of these ophthalmic diseases, the most valuable preventive action is timely detection and treatment by an ophthalmologist. However, over 50% of glaucoma cases go undetected due to lack of timely assessment by a medical expert. This thesis seeks to transform artificial intelligence (AI) into a trustworthy partner to clinicians, aiding in expediting diagnostic screening for obvious cases and serving as corroboration/a ‘second opinion’ in ambiguous cases. In order to develop AI algorithms that can be trusted as team-mates in the clinic, the AI must be robust to data collected at various sites/from various patient populations, its decision-making mechanisms must be explainable, and to benefit the broadest population (for whom expensive imaging equipment and/or specialist time may not be available), it must be portable. This thesis addresses these three challenges (1) by developing and evaluating robust deep learning (DL) algorithms for detection of glaucoma and AMD from data collected at multiple sites or using multiple imaging modalities, (2) by making AI interpretable, through: (a) comparison of image concepts used by DL systems for decision-making with image regions fixated upon by human experts during glaucoma diagnosis, and (b) through odds ratio ranking of clinical biomarkers most indicative of AMD risk used by both experts and AI, and (3) by enhancing theimage quality of data collected via a portable OCT device using deep-learning based super-resolution generative adversarial network (GAN) approaches. The resulting robust deep learning algorithms achieve accuracy as high as 95% at detection of glaucoma and AMD from optical coherence tomography (OCT) and OCT angiography images/volumes. The interpretable AI-concept/expert-eye-movement comparison showed the importance of three OCT-report sub-regions used by both AI and human experts for glaucoma detection. The pipeline described here for evaluating AI robustness and validating interpretable image concepts used by deep learning systems in conjunction with expert eye movements has the potential to help standardize the acceptance of new AI tools for use in the clinic. Furthermore, the eye movement collection protocols introduced in this thesis may also help to train current medical residents and fellows regarding key features employed by expert specialists for accurate and efficient eye disease diagnosis. The odds ratio ranking of AMD biomarkers distinguished the top two clinical features (choroidal neovascularization and geographic atrophy) most indicative of AMD risk that are agreed upon by both AI and experts. Lastly, GAN-based super-resolution of portable OCT images boosted performance of downstream deep learning systems for AMD detection, facilitating future work toward embedding AI algorithms within portable OCT systems, in order for a larger population to gain access to potentially sight-saving technology. By enhancing AI robustness, interpretability, and portability, this work paves the way for ophthalmologist-AI teams to achieve augmented performance compared to human experts or AI alone, leading to expedited eye disease detection, treatment, and thus better patient outcomes

CONVERGENCE ANALYSIS OF A CASCADE ARCHITECTURE NEURAL NETWORK

In this paper, we presen! a mathematical foundation, inchtding a convergence analysis, for cascading architecture neural net works. From this, a mathematical foundation for the cascade correlation learning algorithm can also be found, Furthermore, it becomes apparent that the cascade correlation scheme IS a special case of an efficient hardware learning algorithm called Cascade Error Projection. Our atia[ysis also shows that the convergence of the cascade architecture neural nenvork is assured because it satisfies a Liapunov criterion, in an added hidden unit domain rather than in the time domain Moreover, this analysis ako aI[ows us to predict that other methods (such as the conjugate gradient descent and Newlon’s second order) are good candidates as additional learning techniques. The $na [ choice of a learning technique depends cm the constraints of the problems (e.g., speed, performance, and hardware implementation) which may make one technique much more suitab[e than others. Simulation results help to validate the proposed CEP learning algorithm developed in this paper. 1