SystemX

Two key trends are revolutionizing the way humans conduct spaceflight, namely, the miniaturization of satellites (e.g., micro- and nano-satellites) and the distribution of payload tasks among multiple coordinated units (e.g., formation-flying, on-orbit servicing, fractionation, swarms). The combination of these approaches promises breakthroughs in space science (e.g., imaging of earth-like planets, characterization of gravitational waves), remote sensing (e.g., synthetic aperture radar interferometry, aeronomy, gravimetry), and space exploration (e.g., lifetime extension, assembly of structures, space debris removal). Irrespective of the specific application, future miniature distributed space missions require a high level of autonomy to maintain and reconfigure the relative motion of the participating vehicles within the prescribed accuracy and range of operations. Especially on small spacecraft, these requirements are hard to meet due to the limited resources, and the chief goal of current research and development is to pave the way for the autonomous Guidance, Navigation, & Control (GN&C) of "self-driving nanosatellites."

Leveraging the presenter's experience and contributions to recent satellite formation-flying and rendezvous missions in low earth orbit (TanDEM-X, Prisma, Biros), this presentation addresses the navigation algorithms under developments to enable a new class of miniature distributed space instruments (Starling, Visors, Swarm-Ex). The focus will be on autonomous vision-based and radio-frequency spaceborne absolute and relative navigation systems, including their training and validation using high-fidelity hardware-in-the-loop simulations and robotic testbeds at the Stanford's Space Rendezvous Laboratory.

Over the past decade, machine learning algorithms have been deployed in many cloud-centric applications. However, as the application space continues to grow, various algorithms are now embedded "closer to the sensor" and in wearable devices, eliminating the latency, privacy and energy penalties associated with cloud access. In this talk, I will review mixed-signal circuit techniques that can improve the energy efficiency of moderate-complexity, low-power machine learning inference algorithms. Specific examples include analog feature extraction for image and audio processing, as well as mixed-signal compute circuits for convolutional neural networks.

Industry 4.0 and smart manufacturing will be a critical driver of digital transformation. This inter-disciplinary domain can bring significant quality, productivity and cost benefits across sectors. Today's manufacturing environment is replete with hundreds of signals with petabytes of data. To get the highest ROI from Industry 4.0, the critical success-factor is to make sense of these signals. This seminar will focus on benefits and challenges of system-level ultra-low power sensing, ingesting high-frequency signals, as well as high SNR inferencing with AI/ML.

As society progresses towards increasing pervasive computing levels, I design and build technology-enabled solutions to repurpose everyday devices to help people build resilience and grow wellbeing. I leverage biological and behavioral knowledge to design systems that balance user needs and health outcomes while mitigating surveillance and agency risks. In this talk, I present my research on efficacious and engaging sensors and interventions necessary in the population and public health domains. I share a series of research projects exploring and validating novel ideas on passive sensors - less dependent on subjective surveys or wearables - and subtle interventions that minimize workflow disruption. I show the promise of repurposing existing signals from computing peripherals (i.e., mouse and trackpad) or cars (steering wheel) into "sensorless" sensors and repurposing existing media as just-in-time micro-interventions that can work across multiple scenarios and populations. I discuss how these data could be used in collaboration with domain experts to study topics as varied as the interaction between stress and productivity in office workers, burnout prevention among clinical practitioners, or the prevention of depression among rural health workers. Finally, grounded in theories from neuroscience and behavioral economics, I propose the evolution of everyday "mundane" devices, such as chairs, desks, cars, or even urban lights, into adaptive and autonomous wellbeing-optimizing interventions. I close with a discussion of the research needed to systematically study ethics in pervasive technology for resilience and wellbeing.

Innovations in quantum science and technology have enabled previously unforeseen levels of measurement and control over simple quantum systems comprised of atoms, electrons and photons. This talk will illustrate some of these advances with examples relevant to bio-imaging and precision sensing. For example, so-called quantum non-demolition measurements provide a foundation for next generation electron microscopes capable of low-damage imaging of biological samples such as proteins. Related non-demolition protocols for atoms in high finesse optical cavities are used to produce entangled states of more than 1000 ultra-cold atoms. These states are exploited to improve the performance of atomic clocks. Finally, atom de Broglie wave interference experiments with atomic wavepackets separated by distances as large as 54 cm (image below is the resulting interference pattern) are being used for new tests of gravity and quantum mechanics. These examples, and their possible impact on basic science and technology, will be discussed.

For this workshop, we are gathering visionaries and leaders of industry and academia to give us their perspectives followed by an open discussion. The audience will be Stanford faculty and students, and members of the Stanford SystemX Alliance. All talks will be by-invitation only.

Background: Because there is a shift from 2D scaling to a plethora of new materials and new devices for further performance gains, plus a concomitant shift from system-on-chip (SoC) to heterogeneous integration, chiplet partitioning, and advanced packaging, the roadmap ahead for the semiconductor industry is not as straightforward as it was in the past. Yet, there is tremendous value to industry and academia to know what the future of semiconductor technology will look like, and use that knowledge to plan for the future. The goal of this workshop is to have a forum for sharing perspectives and viewpoints on what future may hold in their respective domains. It is hoped that everyone in the food chain will gain from understanding the opportunities and challenges of an abstraction layer above and an abstraction layer below.

Gallium nitride (GaN) nanoelectronics have operated at temperatures as high as 1000°C making it a viable platform for robust space-grade ("tiny-but-tough") sensorsand electronics. In addition, there has been a tremendous amount of research and industrial investment in GaN as it is positioned to replace silicon in the billion-dollar (USD) power electronics industry, as well as the post-Moore microelectronics universe. Furthermore, the 2014 Nobel Prize in physics was awarded for pioneering research in GaN that led to the realization of the energy-efficient blue light-emitting diode (LED). Even with these major technological breakthroughs, we have just begun the "GaN revolution." New communities are adopting this nanoelectronic platform for a multitude of emerging device applications including the following: sensing, energy harvesting, actuation, and communication. In this talk, we will review and discuss the benefits of GaN's two-dimensional electron gas (2DEG) over silicon's p-n junction for space exploration applications (e.g., radiation-hardened, temperature-tolerant Venus instrumentation). In addition, we will discuss recent results that advance this nanoelectronic device platform for extreme-environment Internet-of-things (IoT)sensors for combustion and down-hole monitoring.

In 1965 Gordon Moore, co-founder of Fairchild Semiconductors and also Intel Corporation, predicted that the number of transistors per die would double every year until 1975 if specific actions were taken to eliminate any inflection points. This represents the first example of a long-term roadmap for semiconductors.

By 1975 data showed his predictions to be correct. He then predicted that the number of transistors would double every two years for the foreseeable future if appropriate actions were taken. Following Gordon Moore's demonstrated success of the roadmap methodology, the National Technology Roadmap for Semiconductors (NTRS) was formed in 1991 in the US; the roadmap became international (ITRS) in 1998 to collectively address a major inflection point (i.e., the end of the planar MOS silicon gate process). Following the ITRS recommendations the semiconductor industry sequentially developed in a very timely fashion Strained Silicon, High-K/Metal-Gate and FinFET into manufacturing. Beginning with 2016 the scope of the roadmap was extended to also include System and Architecture as well as trends on Cryogenic Electronics and Quantum Information Processing. In the not-too-distant future 2D space in ICs will reach fundamental limits and fully utilization of the third dimension (3D) to increase "functionality density" has already started. Furthermore homogeneous and heterogeneous integration of multiple technologies is already in progress and will become the way of the future. New recommendations for actions to be taken in the next 15 years have been formulated and will be presented.

Ethics is often messy and chaotic—it's hard to see the method in the madness. In contrast to the way ethics is usually taught, this talk will offer a new framework that lays out the process or ethics workflow, in order to systematically think through the issues in technology ethics, especially AI ethics. We will also look at a few AI case studies in applying this framework.

The success of the abstract model of computation, in terms of bits, logical operations, algorithms, and programming language constructs makes it easy to forget that computation is a physical process. Our cherished notions of computation and information are grounded in classical mechanics, but the physics of our universe is quantum. A natural question to ask is how computation would change if we adopted a quantum mechanical, instead of a classical mechanical, model of computation. In the early 80s, Richard Feynman, Yuri Manin, and others recognized that certain quantum effect could not be simulated efficiently on conventional computers. This observation led researchers to speculate that perhaps such quantum effect could be used to speed up computation more generally. Slowly, a new picture of computation arose, one that gave rise to a variety of faster algorithms, novel cryptographic mechanisms, and alternative methods of communication. In the first part of the talk, I will introduce key concepts underlying quantum computing and correct misconceptions. In the second part of the talk, I will discuss research being done by NASA's QuAIL group on quantum algorithms, quantum supremacy, elucidating quantum resources for computation, quantum programming and compilation, quantum-inspired classical algorithms, and assessing future applications of quantum computing, all within the broader context of the rapidly evolving field.