Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
FPGA-RICH is an FPGA-based online partial particle identification system for the NA62 experiment employing AI techniques. Integrated between the readout of the Ring Imaging Cherenkov detector (RICH) and the low-level trigger processor (L0TP+), FPGARICH implements a fast pipeline to process in real-time the RICH raw hit data stream, producing trigger primitives containing elaborate physics information—e.g., the number of charged particles in a physics event—that L0TP+ can use to improve trigger decision efficiency. Deployed on a single FPGA, the system combines classical online processing with a compact Neural Network algorithm to achieve efficient event classification while managing the challenging ∼10 MHz throughput requirement of NA62. The streaming pipeline ensures ∼1 μs latency, comparable to that of the NA62 detectors, allowing its seamless integration in the existing TDAQ setup as an additional detector. Development leverages High-Level Synthesis (HLS) and the open-source hls4ml package software–hardware codesign workflow, enabling fast and flexible reprogramming, debugging, and performance optimization. We describe the implementation of the full processing pipeline, the Neural Network classifier, their functional validation, performance metrics and the system’s current status and outlook....
This paper presents NeuroAdaptiveNet, an FPGA-based neural network framework that dynamically self-adjusts its architectural configurations in real time to maximize performance across diverse datasets. The core innovation is a Dynamic Classifier Selection mechanism, which harnesses the k-Nearest Centroid algorithm to identify the most competent neural network model for each incoming data sample. By adaptively selecting the most suitable model configuration, NeuroAdaptiveNet achieves significantly improved classification accuracy and optimized resource usage compared to conventional, statically configured neural networks. Experimental results on four datasets demonstrate that NeuroAdaptiveNet can reduce FPGA resource utilization by as much as 52.85%, increase classification accuracy by 4.31%, and lower power consumption by up to 24.5%. These gains illustrate the clear advantage of real-time, per-input reconfiguration over static designs. These advantages are particularly crucial for edge computing and embedded applications, where computational constraints and energy efficiency are paramount. The ability of NeuroAdaptiveNet to tailor its neural network parameters and architecture on a per-input basis paves the way for more efficient and accurate AI solutions in resource-constrained environments....
The controllable transport of droplets on solid surfaces is crucial for many applications, from water harvesting to bio-analysis. Herein, we propose a novel droplet transport controlling method, reconfigurable orbital electrowetting (ROEW) on inclined slippery liquid-infused porous surfaces (SLIPS), which enables controllable transport and dynamic handling of droplets by non-contact reconfiguration of orbital electrodes. The flexible reconfigurability is attributed to the non-contact wettability modulation and reversibly deformable flexible electrodes. ROEW graphically customizes stable wettability pathways by real-time and non-contact printing of charge-orbit patterns on SLIPS to support the continuous transport of droplets. Benefiting from the fast erase-writability of charges and the movability of non-contact electrodes, ROEW enables reconfiguration of the wetting pathways by designing electrode shapes and dynamically switching electrode configurations, achieving controllable transport of various pathways and dynamic handling of droplet sorting and mixing. ROEW provides a new approach for reconfigurable, electrode-free arrays and reusable microfluidics....
This study presents a reconfigurable optical convolutional neural network (CNN) architecture that integrates a crossbar switch network into a smart-pixel-based optical CNN (SPOCNN) framework. The SPOCNN leverages smart pixel light modulators (SPLMs), enabling high-speed and massively parallel optical computation. To address the challenge of data rearrangement between CNN layers—especially in multi-channel and deep-layer processing—a crossbar switch network is introduced to perform dynamic spatial permutation and multicast operations efficiently. This integration significantly reduces the number of processing steps required for core operations such as convolution, max pooling, and local response normalization, enhancing throughput and scalability. The architecture also supports bidirectional data flow and modular expansion, allowing the simulation of deeper networks within limited hardware layers. Performance analysis based on an AlexNet-style CNN indicates that the proposed system can complete inference in fewer than 100 instruction cycles, achieving processing speeds of over 1 million frames per second. The proposed architecture offers a promising solution for real-time optical AI applications. The further development of hardware prototypes and co-optimization strategies between algorithms and optical hardware is suggested to fully harness its capabilities....
Particle detectors at accelerators generate large amounts of data, requiring analysis to derive insights. Collisions lead to signal pile-up, where multiple particles produce signals in the same detector sensors, complicating individual signal identification. This contribution describes the implementation of a deep-learning algorithm on a Versal Adaptive Compute Acceleration Platform (ACAP) device for improved processing via parallelization and concurrency. Connected to a host computer via Peripheral Component Interconnect express (PCIe), this system aims for enhanced speed and energy efficiency over Central Processing Units (CPUs) and Graphics Processing Units (GPUs). In the contribution, we will describe in detail the data processing and the hardware, firmware and software components of the system. The contribution presents the implementation of the deeplearning algorithm on a Versal ACAP device, as well as the system for transferring data in an efficient way....
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