X-ray Beamline Control with an Online Model for Automated Tuning and Reconfiguration
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An ionic redox transistor comprises a solid channel, a solid reservoir layer, and a solid electrolyte layer disposed between the channel and the reservoir layer. The channel exhibits a substantially linear current-voltage relationship in a first range of voltages, and a nonlinear current-voltage relationship in a second range of voltages that is greater than the first range of voltages. One or both of the substantially linear current-voltage relationship or the nonlinear current-voltage relationship of the channel is varied by changing the concentration of ions such as oxygen vacancies in the channel. Ion or vacancy transport between the channel and the reservoir layer across the electrolyte layer occurs in response to applying a voltage between the channel and the reservoir layer. Subject to the first range of voltages, the channel can function as a synapse device. Subject to the second range of voltages, the channel can function as a neuron device.
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We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.
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This paper presents the application of a new design of a multiport, modular, medium-voltage power electronics hub (M3PE-HUB) in a microgrid setting. The M3PE-HUB system was modeled in a digital real-time simulator (DRTS) and integrated into the Banshee microgrid test system. This paper presents the preliminary DRTS simulation-based results of the M3PE-HUB system connected in a test microgrid system. Verification and validation of the M3PE-HUB architecture and controls in the test microgrid setting are the primary contributions of this work. The results of the operation during the islanding and resynchronization process indicate the feasibility of the proposed architecture in a microgrid setting. This paper also presents results for a system reconfiguration use case where the M3PE-HUB was used to reconfigure the system under a fault condition.
The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.
Migration, division, and reconfiguration - functions essential to living systems - are driven by active processes. Developing synthetic mimics is an outstanding challenge. Lipid bilayers that bound natural systems are locally deformed by active species, e.g., microtubules, but the resulting non-equilibrium shapes relax when active species motion ceases, and the shape changes lack immediate control. A fully synthetic system is described, driven by active particles encapsulated by a reconfigurable nanoparticle-surfactant membrane that undergoes shape fluctuations reminiscent of living cells. These shape changes are preserved after particle activity stops. Surfactant concentration tunes the interfacial tension over three orders of magnitude, making on-demand shape evolution possible. Directional migration, division, and reconfiguration across multiple scales are possible, leading to a new class of biomimetic, reconfigurable, and responsive materials, paving the way for autonomous synthetic machines.
Abstract Physically intelligent micro‐robotic systems exploit information embedded in micro‐robots, their colloidal cargo, and their milieu to interact, assemble, and form functional structures. Nonlinear anisotropic fluids such as nematic liquid crystals (NLCs) provide untapped opportunities to embed interactions via their topological defects, complex elastic responses, and ability to dramatically restructure in dynamic settings. Here a four‐armed ferromagnetic micro‐robot is designed and fabricated to embed and dynamically reconfigure information in the nematic director field, generating a suite of physical interactions for cargo manipulation. The micro‐robot shape and surface chemistry are designed to generate a nemato‐elastic energy landscape in the domain that defines multiple modes of emergent, bottom‐up interactions with passive colloids. Micro‐robot rotation expands the ability to sculpt interactions; the energy landscape around a rotating micro‐robot is dynamically reconfigured by complex far‐from‐equilibrium dynamics of the micro‐robot's companion topological defect. These defect dynamics allow transient information to be programmed into the domain and exploited. Robust micro‐robotic manipulation strategies are demonstrated that exploit these diverse modes of nemato‐elastic interaction to achieve cargo docking, transport, release, and assembly of complex reconfigurable structures at multi‐stable sites. Such structures are of great interest to future developments of LC‐based advanced optical device and micro‐manufacturing in anisotropic environments.
Orbital degrees of freedom offer a largely untapped route to emergent dynamical phenomena in correlated quantum materials. However, it remains unclear whether collective orbital states can intrinsically generate both reactive and memory functionalities in a bulk system. Here we show that in the ferrimagnet Mn₃Si₂Te₆, nonequilibrium reconfiguration of chiral orbital currents produces both emergent inductance and nonvolatile memristance as intrinsic properties of a single crystal. At low frequency and under a magnetic field along the c axis, coherent orbital-current domains generate robust clockwise inductive I-V loops. At higher frequency and low field, current-driven first-order reconfiguration leads to incomplete reversal and metastable trapping, producing an intrinsic electromotive force and a finite remanent voltage at zero current. These results establish orbital currents as a class of quantum state variables that encode both reactive and memory functionalities, opening routes toward intrinsically reconfigurable and energy-efficient electronic systems.
Engineering the properties of electromagnetic wavefronts has become essential to imaging, wireless security, sensing, and wireless communication. In particular, wavefronts that exhibit low spatial coherence can enable sensing functionalities with high accuracy and low latency. The typical use of such wavefronts cannot take advantage of these possibilities, as they require the ability to dynamically reconfigure the wavefront in a controllable and repeatable fashion, over a broad spectral bandwidth. Here, we propose a new approach for generating broadband reconfigurable wavefronts which not only exhibit low spatial coherence at a particular frequency, but are also decorrelated with the wavefronts simultaneously generated at other frequencies. We demonstrate that this frequency-domain decorrelation is a key feature that, in combination with dynamic reconfigurability, enables localization measurements with an order-of-magnitude improvement in accuracy compared to the state of the art.
To understand and resolve adsorption, reconfiguration, and equilibrium conformations of charged star copolymers, we carried out an integrated experimental and coarse-grained molecular dynamics simulation study of the assembly process at the oil–water interface. This is important to guide development of novel surfactants or amphiphiles for chemical transformations and separations. The star block copolymer consisted of arms that are comprised of hydrophilic–hydrophobic block copolymers that are covalently tethered via the hydrophobic blocks to one point. The hydrophobic core represents polystyrene (PS) chains, while the hydrophilic corona represents quaternized poly(2-vinylpyridine) (P2VP) chains. The P2VP is modeled to become protonated when in contact with an acidic aqueous phase, thereby massively increasing the hydrophilicity of this block, and changing the nature of the star at the oil–water interface. This results in a configurational change whereby the chains comprising the hydrophilic corona are significantly stretched into the aqueous phase, while the hydrophobic core remains solubilized in the oil phase. In the simulations, we followed the kinetics of the anchoring and assembly of the star block copolymer at the interface, monitoring the lateral assembly, and the subsequent reconfiguration of the star via changes in the interfacial tension that varies as the degree-of-protonation increases. At low fractions of protonation, the arm cannot fully partition into the aqueous side of the interface and instead interacts with other arms in the oil phase forming a network near the interface. These insights were used to interpret the non-monotonic dependence of pH with the asymptotic interfacial tension from pendant drop tensiometry experiments and spectral signatures of aromatic stretches seen in vibrational sum frequency generation (SFG) spectroscopy. We describe the relationship of interfacial tension to the star assembly via the Frumkin isotherm, which phenomenologically describes anti-cooperativity in adsorbing stars to the interface due to crowding. Although our model explicitly considers long-range electrostatics, the contribution of electrostatics to interfacial tension is small and brought about by strong counterion condensation at the interface. Finally, these results provide key insights into resolving the adsorption, reconfiguration, and equilibrium conformations of charged star block copolymers as surfactants.
Coarse-grained reconfigurable arrays (CGRAs) provide higher flexibility than application-specific integrated circuits (ASICs) and higher efficiency than fine-grained reconfigurable devices such as Field Programmable Gate Arrays (FPGAs). However, CGRAs are generally designed to support offloading of a single kernel. While their design, based on communicating functional units, appears to naturally suit data streaming applications composed of multiple cooperating kernels, current approaches only statically partition the resources across application kernels. However, emerging streaming applications at the edge (scientific instruments, sensor networks, network processing) perform much more than digital signal processing and often are data and input dependent. This leads to extremely variable kernel execution times, severely impacting the throughput of the entire pipeline if resources are only statically allocated. Therefore, in this paper, we propose DRIPS — a coarse-grained, dynamically, and partially reconfigurable array for data-dependent streaming applications. We present a unified compiler framework to facilitate the mapping of a given streaming application onto the DRIPS CGRA architecture. The experimental results show that DRIPS achieves an average throughput improvement of 1.46$\times$ across a set of representative applications over a statically partitioned solution. The additional area overhead to enable dynamic rebalancing consumes 16.34% of the entire area for a 5x5 CGRA prototype.
oarse-grained reconfigurable arrays (CGRAs) are a promising solution to enable energy-efficient acceleration of applications from different domains. By leveraging reconfiguration at the functional level, they can adapt to significantly different computational patterns. Existing CGRA mapping approaches extract instruction-level parallelism, exploit loop-pipelining opportunities, guarantee the data dependency, and target high throughput of a given loop. However, the recurrence data-dependency in the DFG and the mismatch between required and available computing/communication resources complicate the mapping, and might lead to significant unbalances in the utilization of the CGRA's tiles. This results in wasted power for tiles with low utilization. Applying dynamic voltage and frequency scaling (DVFS) can potentially solve this challenge and improve energy efficiency by adjusting voltage and frequency of different tiles independently. CGRAs have also been successful in accelerating data-dependent streaming applications. However, in these applications, the execution time of each kernel in the pipeline might dynamically vary depending on the characteristics of the input. This also leads to under-utilization of resources for the dynamically changing kernels that do not limit the application throughput. DVFS can also improve energy efficiency for these applications by dynamically changing the voltage and frequency levels of tiles that host non performance-constraining kernels. This paper proposes ICEDTEA -- an integrated DVFS-aware framework to map applications on CGRAs that support power islands. ICEDTEA proposes a CGRA architecture supporting DVFS islands at varying granularity (from a single tile to a group of tiles) and the related DVFS-aware compilation and mapping toolchain. ICEDTEA is the first work that introduces DVFS support for spatio-temporal CGRAs at power-island levels. The experimental evaluation shows that ICEDTEA improves average utilization by 2.3$\times$ and energy-efficiency by 1.32$\times$ over a conventional CGRA. With streaming applications, ICEDTEA improves energy efficiency by 1.12$\times$ over a state-of-the-art CGRA that introduces partial dynamic reconfiguration to adapt to variations in kernels' throughput.
The increasing size and complexity of deep learning (DL) models have led to the wide adoption of distributed training methods in datacenters (DCs) and high-performance computing (HPC) systems. However, communication among distributed computing units (CUs) has emerged as a major bottleneck in the training process. In this study, we propose Flex-SiPAC, a flexible silicon photonic accelerated compute cluster designed to accelerate multi-tenant distributed DL training workloads. Flex-SiPAC takes a co-design approach that combines a silicon photonic hardware platform with a tailored collective algorithm, optimized to leverage the unique physical properties of the architecture. The hardware platform integrates a novel wavelength-reconfigurable transceiver design and a micro-resonator-based wavelength-reconfigurable switch, enabling the system to achieve flexible bandwidth steering in the wavelength domain. The collective algorithm is designed to support reconfigurable topologies, enabling efficient all-reduce communications that are commonly used in DL training. The feasibility of the Flex-SiPAC architecture is demonstrated through two testbed experiments. First, an optical testbed experiment demonstrates the flexible routing of wavelengths by shuffling an array of input wavelengths using a custom-designed spatial-wavelength selective switch. Second, a four-GPU testbed running two DL workloads shows a 23% improvement in job completion time compared to a similarly sized leaf-spine topology. We further evaluate Flex-SiPAC using large-scale simulations, which show that Flex-SiPAC is able to reduce the communication time by 26% to 29% compared to state-of-the-art compute clusters under representative collective operations.
The following report serves as a summary of the accomplishments and testing results by the Natural convection Shutdown heat removal Test Facility (NSTF) experimental program over the past 12-month period. A major activity included reconfiguration of the chimney piping geometry which altered the discharge position into the tank from the original 50% tank height to a new lower position at a 10.9% tank height. This modification increases the volume of available coolant thus extending long-term operating capacity, however also results in a decreased liquid driving head which has the potential to reduce the natural circulation efficiency and decrease overall performance. Examination of the tradeoffs for these two configurations is an area of interest for RCCS designers and drove planned test activities this year. Ten matrix tests were performed in FY24, totaling 195 hours of heated operations and 12.9 MWh of electrical heating throughout the year, with eight classified as Accepted per NQA-1 and two classified as Trending. Testing prior to the facility reconfiguration examined the facility response to throttling at the tank inlet, demonstrating increased sensitivity of flow instabilities to throttling within the two-phase region when compared to throttling at the lower sensitivity single-phase inlet region. Testing after the chimney reconfiguration began with a baseline test at conditions of 80% inventory fill and prototypic thermal power input of 2.1 MW t . In addition to establishing a reference for nominal system behavior and performance, repeat testing with multiple subsequent runs demonstrated strong repeatability between tests performed at the same conditions in this new configuration. These tests also examined if influences would occur to system behavior with installation of new higher-resolution instrumentation within the upper chimney. In this critical region where boiling and flashing phenomena dominate, the test results provided confidence that the new instrumentation does not uncharacteristically influence the observed behaviors. An inventory parametric series was then initiated to examine system behavior with the new chimney configuration at six varying initial inventory levels, ranging from high 80% to low 20% fill. Two-phase oscillations with similar peak and mean flow rates were observed when comparing to the previous mid tank configuration. Generally, similar system response trends with inventory were also observed, such as two phase oscillations suppressing as inventories were lowered. However, in one absolute inventory comparison at the highest fill of 80%, flow oscillations saw a gradual growth over the 4 hours of two-phase operation in the new lower tank inlet configuration, opposite of the gradual dampening observed in the mid tank inlet configuration. This can be attributed to, in part, a greater hydrostatic head pressure above the two-phase discharge region where the boiling front is developed. Furthermore, the change resulted in greater sensitivity to liquid degassing phenomena during single-phase heating, causing loop instabilities to form which trigged moderate flow degradation during the period approaching saturation and boiling conditions. This behavior had been observed previously under some conditions but was a common occurrence in recent testing with the newer lower tank inlet configuration. Initial observations from these first data sets suggest an overall larger window of stability for the mid tank inlet configuration compared to the lower tank inlet. Lastly, the lowest inventory fill test was repeated over an extended testing window to examine depletion behavior. Natural circulation flow and effective heat removal performance were observed during most of the testing period; only after the tank became fully drained (0% fill) did flow stagnate and violent geysering events occur. This early observation confirms one relative advantage over the mid tank inlet configuration, which stagnated under comparable conditions at ~20% inventory remaining in the tank.
Using a control variable, the functionality of Polymorphic circuits can be modified, making them adaptable and useful for reconfiguring circuit behavior — all the way from gate level to system level. State-of-the art polymorphic circuits are based on custom non-linear circuit design or emerging devices such as ambipolar FET, configurable magnetic devices etc. While some of these approaches are inefficient in performance, others involve exotic devices. The Crosstalk computing based polymorphic circuits offer a fresh perspective. In Crosstalk, the interconnect interference between nanoscale metal lines is intentionally engineered to exhibit the programmable Boolean logic behavior. This approach relies on the coupling between metal lines and not on the transistors for computing, resulting in better scalability, security by obscurity, and fault tolerance by reconfiguration. Our novel approach is backed by the mathematical formulation that conveys the rationale to generalize and achieve a wide variety of polymorphic circuits. Our experiments, including design, simulation, and Power Performance Area (PPA) characterization results indicate that crosstalk circuits provide significant improvement in transistor count (about 3x), switching energy (2x), and speed (1.5x) for polymorphic logic circuits. In the best-case scenario, the transistor count reduction is 5x. This paper presents Crosstalk computing’s fundamentals, polymorphism and the scalability aspects to compete/co-exist with CMOS for digital logic implementations below 10 nm. Our scalability study uses Open Source 7 nm PDK, considers all process variation aspects and accommodates worst-case scenarios. The study results for various benchmark circuits show that the Crosstalk technology is a viable alternative to CMOS for digital logic implementations below 10 nm, having 48% density, 57% power, and 10% performance gains over equivalent CMOS counterparts. Finally, we compare Crosstalk Polymorphic Circuit design technique with similar approaches described in related works and discuss its features and constraints.
Most microgrid protection schemes found in published literature suffer from a lack of generality in that they work well for the assumed topology, including type and placement of sources. Other generic protection schemes tend to be too complicated, too expensive, or both. To overcome these draw- backs, a topology-agnostic, scalable, and cost-aware protection based on fundamental principles that work in the presence of high penetration of inverter-based resources (IBRs) is developed and tested in this paper. Here, the protection system also implements stable automatic reconfiguration of the healthy sections of the system after clearance of fault, thus increasing resilience by self- healing. To achieve this ambitious goal, stable inverter models are developed that operate in unbalanced networks in grid-connected and islanded modes, even with 100% IBRs, share power without conflicting controls, and can ride through faults while limiting fault currents. The scheme is tested for primary and backup protection and reconfiguration on the IEEE 123-node feeder in grid-connected and islanded modes with 15 IBRs connected to the system.