This paper presents a real-time geometry-informed control strategy to homogenize melt pool measurements in laser powder bed fusion (L-PBF) using reinforcement learning. The learning control strategy incorporates geometric information of the scan path as well as in-situ melt pool measurements to compute the laser power signal for reducing in-process melt pool inhomogeneities. First, we design and validate a data-driven model to train the reinforcement learning agent in simulation, with the goal of reducing the amount of experimental data needed for training. Using this simulation-based training approach has the added benefit of avoiding unsafe or infeasible experiments, an issue that is often encountered in training the reinforcement learning agent. After training, the learned control strategy attenuates the 1-norm error by$\mathbf{37\%}$and standard deviation by$\mathbf{39\%}$in simulation. We then deploy this learned control strategy in an experimental test bed for a new scan geometry. In this test scenario, the policy achieves a$\mathbf{30\%}$reduction in error, and a$\mathbf{36\%}$reduction in melt pool signal variation, thereby illustrating the potential of reinforcement learning in real-time geometry-agnostic control for L-PBF. Finally, we demonstrate that the reinforcement learning agent delivers the same level of performance as a model-based feedforward controller with PID feedback, with 20$\times$less computational time for a single geometry.Note to Practitioners—This work was motivated by the need to develop a practical control algorithm for L-PBF systems. Because L-PBF systems manufacture customized on-demand geometries, it is critical that the control strategy is extendable to and easily optimized for each geometry. Specifically, this effort develops an efficient and robust reinforcement learning control algorithm that can be used across novel part geometries, once trained. The control strategy is designed using a simulation-to-real approach, which is key for avoiding extensive training effort and avoids unsafe training experiments.

In this paper, an industry-level new-generation EDA solution for reliability-aware design in nanoscale FinFET technology is presented for the first time, with new compact transistor aging models and upgraded circuit reliability simulator. Our work solves various issues found in FinFET silicon data of NBTI aging. Especially, instead of ignoring or less accurate NBTI recovery effect model in traditional simulators, accurate NBTI degradation and recovery models are proposed and validated by silicon data for full stress/recovery range in the FinFET technology. The history effect, one of the important features of NBTI which is missing in the existing industrial tools, is included based on new simulation methodology. Since FinFET reliability data suggests the conventional linear extrapolation method is no longer valid, an accurate fast-speed long-term prediction method is proposed based on smart iteration flows of equivalence. The frequency dependence of NBTI, which draws much attention, is included in the new simulator automatically. This work has been integrated into Cadence reliability simulator, providing designers an opportunity for accurate reliability-aware circuit design.

Growing demand for energy-efficient, high-performance systems has resulted in the growth of innovative heterogeneous computing system architectures that use FPGAs. FPGA-based architectures enable designers to implement custom instruction streams executing on potentially thousands of compute elements. Traditionally, FPGAs have been used as compute elements on PCI devices; however, this does not allow the FPGAs to be co-processors. This paper describes a high-performance system architecture that is based on the Intel® Xeon® platform in which one or more FPGAs, acting as application accelerators, replace one or more processors in a dual/multi-processor (DP/MP) platform. The FPGA is thus connected directly to the Front Side Bus (FSB) and enjoys the same privileges as a processor, i.e., full participation in the coherency protocol, unrestricted access to system memory and to other processors via the high bandwidth, and low latency connection to the FSB. In addition, we also describe a software layer called the "Accelerator Abstraction Layer (AAL)", which provides a uniform, hardware- and/or platform-independent application interface. Applications written on AAL can be ported to multiple platforms that have different types of accelerators and the application does not have to be modified. In addition, the AAL also enables the developer/user to reprogram the FPGA on the fly (analogous to an operating system context switch) thereby utilizing the programmable nature of the FPGA. The resulting hardware/software stack creates a flexible and powerful platform for accelerator innovation and deployment.

Wireless sensor networks offer a wide range of challenges to networking research, including unconstrained network scale, limited computing, memory and energy resources, and wireless channel errors. We study the problem of delivering messages from any sensor to an interested client user along the minimum-cost path in a large sensor network. We propose a new cost field based approach to minimum cost forwarding. In the design, we present a novel backoff-based cost field setup algorithm that finds the optimal costs of all nodes to the sink with one single message overhead at each node. Once the field is established, the message, carrying dynamic cost information, flows along the minimum cost path in the cost field. Each intermediate node forwards the message only if it finds itself to be on the optimal path, based on dynamic cost states. Our design does not require an intermediate node to maintain explicit "forwarding path" states. It requires a few simple operations and scales to any network size. We show the correctness and effectiveness of the design by both simulations and analysis.

Recursive hierarchy provides a framework for extending position estimation throughout a sensor network. Given imprecise ranging and inter-node communication, nodes scattered throughout a large volume can estimate their physical locations from a small set of reference nodes using only local information. System coverage increases iteratively, as nodes with newly estimated positions join the reference set, capitalizing on the massive scale of sensor networks. The system frames position estimation as a geometric problem solvable through common nonlinear regression techniques and develops methods for gauging the reliability of position estimates. This provides a flexible framework that can use and enhance a variety of technologies and protocols to produce fine-grained position estimates. A specific model provides a simulation environment showing that over 90% of position estimates are correct to within 3% of the ranging distance with only 5% of the system in the initial reference set.