Learning Before Filtering: Real-Time Hardware Learning at the Detector Level

TL;DR

This paper introduces a scalable FPGA-based hardware architecture for real-time neural network training directly at the detector level, enabling adaptive, high-throughput data filtering without reliance on pre-defined models.

Contribution

It presents a novel, implementation-independent digital hardware design for in-situ neural network training optimized for high-speed data processing at the detector level.

Findings

FPGA implementation confirms in-situ training preserves accuracy

Current FPGAs can train networks with approximately 3,500 neurons

The architecture balances processing speed, model complexity, and resource use

Abstract

Advances in sensor technology and automation have ushered in an era of data abundance, where the ability to identify and extract relevant information in real time has become increasingly critical. Traditional filtering approaches, which depend on a priori knowledge, often struggle to adapt to dynamic or unanticipated data features. Machine learning offers a compelling alternative-particularly when training can occur directly at or near the detector. This paper presents a digital hardware architecture designed for real-time neural network training, specifically optimized for high-throughput data ingestion. The design is described in an implementation-independent manner, with detailed analysis of each architectural component and their performance implications. Through system parameterization, the study explores trade-offs between processing speed, model complexity, and hardware resource utilization. Practical examples illustrate how these parameters affect applicability across various use cases. A proof-of-concept implementation on an FPGA demonstrates in-situ training, confirming that computational accuracy is preserved relative to conventional software-based approaches. Moreover, resource estimates indicate that current-generation FPGAs can train networks of approximately 3,500 neurons per chip. The architecture is both scalable and adaptable, representing a significant advancement toward integrating learning directly within detector systems and enabling a new class of extreme-edge, real-time information processing.