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Post-quantum cryptography (PQC) refers to cryptographic algorithms that are thought to be secure against a cryptanalytic attack by a quantum computer. Before PQC algorithms can be widely deployed to replace the current standards such as the RSA algorithm, they need to be rigorously evaluated theoretically and practically. In this work, we present a cloud-based infrastructure being developed for performing side-channel analysis on PQC algorithms for the research community. Multiple types of side-channel attacks, such as timing attacks, power attacks, and electromagnetic attacks can be applied on different types of devices, such as FPGA devices and microcontrollers. An automated tool flow is being developed that can run executables on the target devices, collect traces (e.g., power consumption waveforms and electromagnetic radiation signals), perform leakage assessment (using Test Vector Leakage Assessment), and generate analysis reports. Remote users access the infrastructure through a web portal by uploading the hardware or software implementations of cryptographic algorithms. Side-channel attack and leakage analysis are performed on the given implementation. Finally, the user is informed for downloading the analysis report from the portal. 

“Active structures” are physical structures that incorporate real-time monitoring and control. Examples include active vibration damping or blast mitigation systems. Evaluating physics-based models in real-time is generally not feasible for such systems having high-rate dynamics which require microsecond response times, but data-driven machine-learning-based models can potentially offer a solution. This paper compares the cost and performance of two FPGA-based implementations of real-time, continuously-trained models for forecasting timeseries signals with non-stationarities, with one using HighLevel Synthesis (HLS) and the other a programmable overlay architecture. The proposed model accepts a uni-variate vibration signal and seeks to forecast future samples to inform highrate controllers. The proposed forecasting method performs two concurrent neural inference operations. One inference forecasts the state of the signal f samples into the future as a function of the most recent h samples, while the other forecasts the current sample given h samples starting from h + f − 1 samples into the past. The first forecast produces the forecast while the second forecast allows the system to calculate the model’s loss and perform an immediate model update before the next sample period.