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Stack unwinding is a well-established approach for handling panics in Rust programs. However, its feasibility on resource- constrained embedded systems has been unclear due to the associated overhead and complexity. This paper presents our experience of implementing stack unwinding and panic recovery within a Rust-based soft real-time embedded oper- ating system. We describe several novel optimizations that help achieve adequate performance for a ying drone with a CPU overhead of 2.6% and a storage overhead of 26.0% to recover from panics in application tasks and interrupt handlers. 

The Minimum-Weight Perfect Matching (MWPM) decoder is widely used in Quantum Error Correction (QEC) decoding. Despite its high accuracy, existing implementations of the MWPM decoder cannot catch up with quantum hardware, e.g., 1 million measurements per second for superconducting qubits. They suffer from a backlog of measurements that grows exponentially and as a result, cannot realize the power of quantum computation. We design and implement a fast MWPM decoder, called Parity Blossom, which reaches a time complexity almost proportional to the number of defect measurements. We further design and implement a parallel version of Parity Blossom called Fusion Blossom. Given a practical circuit-level noise of 0.1%, Fusion Blossom can decode a million measurement rounds per second up to a code distance of 33. Fusion Blossom also supports stream decoding mode that reaches a 0.7 ms decoding latency at code distance 21 regardless of the measurement rounds. 

A fault-tolerant quantum computer must decode and correct errors faster than they appear. The faster errors can be corrected, the more time the computer can do useful work. The Union-Find (UF) decoder is promising with an average time complexity slightly higher than O(d3). We report a distributed version of the UF decoder that exploits parallel computing resources for further speedup. Using an FPGA-based implementation, we empirically show that this distributed UF decoder has a sublinear average time complexity with regard to d, given O(d3) parallel computing resources. The decoding time per measurement round decreases as d increases, a first time for a quantum error decoder. The implementation employs a scalable architecture called Helios that organizes parallel computing resources into a hybrid tree-grid structure. We are able to implement d up to 21 with a Xilinx VCU129 FPGA, for which an average decoding time is 11.5 ns per measurement round under phenomenological noise of 0.1%, significantly faster than any existing decoder implementation. Since the decoding time per measurement round of Helios decreases with d, Helios can decode a surface code of arbitrarily large d without a growing backlog. 

Microcontrollers are the heart of embedded systems. Due to cost and power constraints, they do not have memory management units (MMUs) or even memory protection units (MPUs). As a result, embedded software faces two related challenges both concerned with the stack. First, in a multi-tasking environment, physical memory used by the stack is usually statically allocated per task. Second, a stack overflow is difficult to detect for lower-end microcontrollers without an MPU. In this work, we argue that segmented stacks, a notion investigated and subsequently dismissed for systems with virtual memory, can solve both challenges for embedded software. We show that many problems with segmented stacks vanish on embedded systems and present novel solutions to the rest. Importantly, we show that segmented stacks, combined with Rust, can guarantee memory safety without MMU or MPU. Moreover, segmented stacks allow memory to be dynamically allocated to per-task stacks and can improve memory efficiency when combined with proper scheduling. 

The National Science Foundation (NSF) Artificial Intelligence (AI) Institute for Edge Computing Leveraging Next Generation Networks (Athena) seeks to foment a transformation in modern edge computing by advancing AI foundations, computing paradigms, networked computing systems, and edge services and applications from a completely new computing perspective. Led by Duke University, Athena leverages revolutionary developments in computer systems, machine learning, networked computing systems, cyber‐physical systems, and sensing. Members of Athena form a multidisciplinary team from eight universities. Athena organizes its research activities under four interrelated thrusts supporting edge computing: Foundational AI, Computer Systems, Networked Computing Systems, and Services and Applications, which constitute an ambitious and comprehensive research agenda. The research tasks of Athena will focus on developing AI‐driven next‐generation technologies for edge computing and new algorithmic and practical foundations of AI and evaluating the research outcomes through a combination of analytical, experimental, and empirical instruments, especially with target use‐inspired research. The researchers of Athena demonstrate a cohesive effort by synergistically integrating the research outcomes from the four thrusts into three pillars: Edge Computing AI Systems, Collaborative Extended Reality (XR), and Situational Awareness and Autonomy. Athena is committed to a robust and comprehensive suite of educational and workforce development endeavors alongside its domestic and international collaboration and knowledge transfer efforts with external stakeholders that include both industry and community partnerships.

 

Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real‐life applications. Artificial intelligence‐enabled inverse design is a powerful tool to realize performance‐oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics‐informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four‐mode analytical models by physics‐informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence‐enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics‐informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real‐life applications is discussed.

 

Massive multiple-input multiple-output (mMIMO) technology uses a very large number of antennas at base stations to significantly increase efficient use of the wireless spectrum. Thus, mMIMO is considered an essential part of 5G and beyond. However, developing a scalable and reliable mMIMO system is an extremely challenging task, significantly hampering the ability of the research community to research nextgeneration networks. This "research bottleneck" motivated us to develop a deployable experimental mMIMO platform to enable research across many areas. We also envision that this platform could unleash novel collaborations between communications, computing, and machine learning researchers to completely rethink next-generation networks. 

Emerging research has begun investigating the neural underpinnings of the biological and psychological differences that drive political ideology, attitudes, and actions. Here, we explore the neurological roots of politics through conducting a large sample, whole-brain analysis of functional connectivity (FC) across common fMRI tasks. Using convolutional neural networks, we develop predictive models of ideology using FC from fMRI scans for nine standard task-based settings in a novel cohort of healthy adults (n = 174, age range: 18 to 40, mean = 21.43) from the Ohio State University Wellbeing Project. Our analyses suggest that liberals and conservatives have noticeable and discriminative differences in FC that can be identified with high accuracy using contemporary artificial intelligence methods and that such analyses complement contemporary models relying on socio-economic and survey-based responses. FC signatures from retrieval, empathy, and monetary reward tasks are identified as important and powerful predictors of conservatism, and activations of the amygdala, inferior frontal gyrus, and hippocampus are most strongly associated with political affiliation. Although the direction of causality is unclear, this study suggests that the biological and neurological roots of political behavior run much deeper than previously thought.