Bio-inspired learning and hardware acceleration with emerging memories

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called action potentials. Motivated by these facts, the Spiking Neural Networks (SNNs), which are a bio-plausible version of neural networks have been proposed as an alternative computing paradigm where the timing of spikes generated by artificial neurons is central to its learning and inference capabilities. This dissertation demonstrates the computational power of the SNNs using conventional CMOS and emerging nanoscale hardware platforms. The first half of this dissertation presents an SNN architecture which is trained using a supervised spike-based learning algorithm for the handwritten digit classification problem. This network achieves an accuracy of 98.17% on the MNIST test data-set, with about 4X fewer parameters compared to the state-of-the-art neural networks achieving over 99% accuracy. In addition, a scheme for parallelizing and speeding up the SNN simulation on a GPU platform is presented. The second half of this dissertation presents an optimal hardware design for accelerating SNN inference and training with SRAM (Static Random Access Memory) and nanoscale non-volatile memory (NVM) crossbar arrays. Three prominent NVM devices are studied for realizing hardware accelerators for SNNs: Phase Change Memory (PCM), Spin Transfer Torque RAM (STT-RAM) and Resistive RAM (RRAM). The analysis shows that a spike-based inference engine with crossbar arrays of STT-RAM bit-cells is 2X and 5X more efficient compared to PCM and RRAM memories, respectively. Furthermore, the STT-RAM design has nearly 6X higher throughput per unit Watt per unit area than that of an equivalent SRAM-based (Static Random Access Memory) design. A hardware accelerator with on-chip learning on an STT-RAM memory array is also designed, requiring $16$ bits of floating-point synaptic weight precision to reach the baseline SNN algorithmic performance on the MNIST dataset. The complete design with STT-RAM crossbar array achieves nearly 20X higher throughput per unit Watt per unit mm^2 than an equivalent design with SRAM memory. In summary, this work demonstrates the potential of spike-based neuromorphic computing algorithms and its efficient realization in hardware based on conventional CMOS as well as emerging technologies. The schemes presented here can be further extended to design spike-based systems that can be ubiquitously deployed for energy and memory constrained edge computing applications

Soft Bootstrap and Supersymmetry

The soft bootstrap is an on-shell method to constrain the landscape of effective field theories (EFTs) of massless particles via the consistency of the low-energy S-matrix. Given assumptions on the on-shell data (particle spectra, linear symmetries, and low-energy theorems), the soft bootstrap is an efficient algorithm for determining the possible consistency of an EFT with those properties. The implementation of the soft bootstrap uses the recently discovered method of soft subtracted recursion. We derive a precise criterion for the validity of these recursion relations and show that they fail exactly when the assumed symmetries can be trivially realized by independent operators in the effective action. We use this to show that the possible pure (real and complex) scalar, fermion, and vector exceptional EFTs are highly constrained. Next, we prove how the soft behavior of states in a supermultiplet must be related and illustrate the results in extended supergravity. We demonstrate the power of the soft bootstrap in two applications. First, for the N= 1 and N=2 CP^1 nonlinear sigma models, we show that on-shell constructibility establishes the emergence of accidental IR symmetries. This includes a new on-shell perspective on the interplay between N=2 supersymmetry, low-energy theorems, and electromagnetic duality. We also show that N=2 supersymmetry requires 3-point interactions with the photon that make the soft behavior of the scalar O(1) instead of vanishing, despite the underlying symmetric coset. Second, we study Galileon theories, including aspects of supersymmetrization, the possibility of a vector-scalar Galileon EFT, and the existence of higher-derivative corrections preserving the enhanced special Galileon symmetry. This is addressed by soft bootstrap and by application of double-copy/KLT relations applied to higher-derivative corrections of chiral perturbation theory.Comment: 71 pages, no figures. v2: significant new material about the N=2 CP^1 NLSM; typos correcte

SYSTEM AND METHOD FOR PROVIDING SECURE BNPL FOR B2B

The present disclosure provides a method and prediction system for providing secure BNPL for B2B using federated blockchain and AI. The prediction system provides shared digital ledger using federated blockchain i.e., by using shared digital ledger, participants can see history and transfer of assets and identify fraudulent transactions easily. Further, the prediction system provides secure BNPL using Deep Learning. The prediction system performs data pre-processing and feature engineering. Further, the prediction system analyses business using machine learning models. Thereafter, the prediction system performs BNPL amount prediction using neural networks and regression. Thus, the present disclosure prevents frauds and provides loaning for secure BNPL for B2B. Figures 3a-3

Crisis, Rupture and Structural Change: Re-imagining Global Learning and Engagement While Staying in Place During the Covid-19 Pandemic

The COVID-19 pandemic led to unprecedented closures of national borders and the withdrawal of much of the social and cultural activities of society into the walls of the home. For us, educators focused on global engagement and analyzing international law and society, the abrupt retreat into the shelter of domestic walls disrupted the very subjects we were studying—inside and outside the classroom. In the pandemic’s first wave, most study abroad and international experiential programs were cancelled indefinitely, and the programs that continued had to operate in an environment of social distancing and uncertainty. We were forced to scramble to accommodate the needs of our students who were suddenly sent home or had travel plans cancelled. At the same time, the global nature of this and other ongoing crises (from humanitarian emergencies that spill across borders to the global impacts of climate change) underscored the need to prepare students for a future where both cross-border crises and the need for international collaboration and education will be heightened. These developments also highlighted the need for a variety of meaningful virtual alternatives for students to acquire the critical skills and knowledge needed to succeed in global and cross-cultural environments. Against this backdrop, in late spring 2020 we turned our focus to developing a course to turn the COVID-19 crisis itself into a virtual international learning opportunity. We aimed to utilize the shared experience of living through a pandemic that was now a global crisis as a starting point for the exploration of global perspectives and responses to crisis, and as a vantage point to help students link their current challenges and experiences to the impact of pandemic in the societies where they had planned to travel for work or study. Isolated in our homes with our own public and domestic lives collapsing and colliding, we aimed to create global connections by creating a space where we and our students could connect the ruptures created by the current crisis to the ruptures and reshaping of perspectives, world views, and personal trajectories that is the hallmark of a transformative global or intercultural encounter. Our goal was to deepen students’ empathetic, contemplative, and communication skills—critical components of global experiential education—while drawing upon literature and pedagogy in these areas and employing experiential learning techniques. The rise of protests related to the Black Lives Matter movement in the middle of the course added a new dimension to our plan and served as a catalyst for both ourselves and our students to move beyond the original course goals and metrics, and to utilize our experiences living through a crisis as to explore how individuals and societies create and grapple with structural change. Similarly, the clashes and re-drawing of lines between our homes, workplaces and classrooms created additional opportunities for connection and to replace reimaging our individual and collective futures. This reflective essay interrogates and records our goals, methods and experiences in creating this classroom space and pedagogical experience during a period of crisis. Ultimately, it memorializes how the experience of developing and teaching this course during the COVID-19 pandemic itself also served as a crucible that reflected the pressures of the pandemic experience, and how our attempts to catalyze change and global engagement for our students were transformative for our own professional and personal trajectories

PITOT-TUBE HEATING IN AIRCRAFTS BY SKIN EFFECT

A pitot-tube is positioned mainly to function the Airspeed Indicator (ASI), Altimeter and the vertical speed indicator (VSI). Any difference in the Pitot tube causes a great change in these flight instruments. The idea of this project is to use skin effect to become distributed within a conductor and attached to the tube overall to maintain the temperature. Skin effect as we describe, is the tendency of an alternating current (AC) to become distributed within a conductor such that current density is largest near the surface of the conductor, and decrease with greater depths within the conductor. The heat produced in the outer heat tube, can be measured and regulated with a microprocessor based, controller. This regulates the temperature reducing the risk of Pitot-tube melting and thereby avoiding pitot-tube icing at higher altitudes. The main idea is to avoid air crashes due to the misreading created due the pitot tube icing. This also lowers the power consumption, increased heating element lifespan and does not endlessly waste electricity when not in icing conditions mainly with the help of the micro controller

Progression of pediatric celiac disease from potential celiac disease to celiac disease: A retrospective cohort study

BACKGROUND: A subset of patients with serology suggesting celiac disease have an initially negative biopsy but subsequently develop histopathologic celiac disease. Here we characterize patients with potential celiac disease who progress to celiac disease. METHODS: We performed a retrospective analysis of children (0-18 years of age) with biopsy-confirmed celiac disease seen at St. Louis Children\u27s Hospital between 2013 and 2018. RESULTS: Three hundred sixteen of 327 (96%) children with biopsy-confirmed celiac disease were diagnosed on initial biopsy. The 11 children with potential celiac disease who progressed to celiac disease had lower anti-tissue transglutaminase (anti-TTG IgA) concentrations (2.4 (1.6-5) X upper limit of normal (ULN) vs. 6.41 (3.4-10.5) X ULN) at time of first biopsy. Their median anti-TTG IgA concentrations rose from 2.4 (1.6-5) X ULN to 3.6 (3.1-9.2) X ULN between biopsies. CONCLUSIONS: Four percent of biopsy confirmed celiac patients initially had a negative biopsy, but later developed histopathologic celiac disease. This is likely an underestimate as no surveillance algorithm was in place. We recommend repeat assessment in children whose serology suggests celiac disease despite normal small bowel biopsy

HybMT: Hybrid Meta-Predictor based ML Algorithm for Fast Test Vector Generation

Testing an integrated circuit (IC) is a highly compute-intensive process. For today's complex designs, tests for many hard-to-detect faults are typically generated using deterministic test generation (DTG) algorithms. Machine Learning (ML) is being increasingly used to increase the test coverage and decrease the overall testing time. Such proposals primarily reduce the wasted work in the classic Path Oriented Decision Making (PODEM) algorithm without compromising on the test quality. With variants of PODEM, many times there is a need to backtrack because further progress cannot be made. There is thus a need to predict the best strategy at different points in the execution of the algorithm. The novel contribution of this paper is a 2-level predictor: the top level is a meta predictor that chooses one of several predictors at the lower level. We choose the best predictor given a circuit and a target net. The accuracy of the top-level meta predictor was found to be 99\%. This leads to a significantly reduced number of backtracking decisions compared to state-of-the-art ML-based and conventional solutions. As compared to a popular, state-of-the-art commercial ATPG tool, our 2-level predictor (HybMT) shows an overall reduction of 32.6\% in the CPU time without compromising on the fault coverage for the EPFL benchmark circuits. HybMT also shows a speedup of 24.4\% and 95.5\% over the existing state-of-the-art (the baseline) while obtaining equal or better fault coverage for the ISCAS'85 and EPFL benchmark circuits, respectively.Comment: 9 pages, 7 figures and 7 tables. Changes from the previous version: We performed more experiments with different regressor models and also proposed a new neural network model, HybNN. We report the results for the EPFL benchmark circuits (most recent and large) and compare our results against a popular commercial ATPG too