Speaker
Description
This project develops a hardware-accelerated, low-latency inference framework for real-time denoising and signal reconstruction in high-throughput, noise-limited measurement systems. While motivated by X-ray Free Electron Laser (XFEL) imaging, the proposed approach is designed to be broadly applicable to a wide range of data-intensive scientific and industrial domains, including plasma diagnostics and control in fusion reactors, nonlinear optical systems, and high-field experimental platforms.
At its core, the project implements a foundation-model-based denoising algorithm using a hardware–algorithm co-design strategy on Groq Language Processing Units (LPUs), enabling deterministic, high-throughput inference. To support real-time operation, the Groq hardware is tightly coupled with an FPGA-based front-end that ensures deterministic, continuous data streaming from sensor systems to the inference engine. This architecture minimizes data transfer overhead and enables fully pipelined processing under demanding acquisition rates.
The main technical objectives include: (i) low-level optimization of neural network primitives—such as convolutions, normalization, and activation functions—tailored to the Groq execution model; (ii) development of a high-throughput streaming interface between FPGA-based data acquisition systems and Groq accelerators; (iii) integration of the denoising engine into closed-loop control frameworks, enabling real-time feedback and adaptive system steering; and (iv) systematic benchmarking of latency, energy efficiency, and reconstruction accuracy against conventional CPU and GPU implementations.
The resulting prototype will demonstrate a generalizable architecture for embedding advanced machine learning inference directly into experimental and operational pipelines. In XFEL environments, this enables real-time image reconstruction and adaptive experiment control, while in fusion research it supports plasma state estimation and feedback-driven stabilization. More broadly, the approach establishes a scalable pathway toward autonomous, AI-enhanced instrumentation capable of self-optimization across diverse high-performance sensing and control applications.