Hardware-Accelerated Line-Rate Bitstream Screening for Secure FPGA Reconfiguration

TL;DR

This paper introduces BLADEI, a hardware-accelerated framework for real-time security screening of FPGA bitstreams, significantly reducing latency and enabling secure dynamic reconfiguration in cloud and edge environments.

Contribution

BLADEI is the first to combine multi-scale byte-sequence learning with hardware acceleration for FPGA bitstream security screening without source code or vendor tools.

Findings

Achieves a macro F1-score of 0.91 in detecting anomalous bitstreams.

Software preprocessing accounts for 92% of total latency in the system.

Hardware acceleration reduces feature extraction latency to milliseconds.

Abstract

As Field-Programmable Gate Arrays (FPGAs) scale in multi-tenant cloud and edge-AI environments, the configuration bitstream has become a critical, yet opaque, security boundary. Existing hardware Trojan detection methods often rely on trusted design artifacts or computationally intensive reverse-engineering, introducing prohibitive latencies in dynamic, "just-in-time" reconfiguration workflows. This paper presents BLADEI (Bitstream-Level Abnormality Detection for Embedded Inference), a bitstream-level security framework designed for deployment-time screening of FPGA configurations without requiring source code, netlists, or vendor-specific tooling. BLADEI introduces a hybrid architecture that combines multi-scale byte-sequence learning with compact statistical representations to detect anomalous configurations directly from raw bitstreams. We implement the framework on a Xilinx PYNQ-Z1 system, demonstrating an end-to-end cloud-to-edge pipeline that enforces security prior to FPGA configuration. Evaluating across 1,383 bitstreams, BLADEI achieves a macro F1-score of 0.91. However, our systems-level characterization reveals a "preprocessing wall": software-based feature extraction accounts for 92% of the total 16.4-second latency, while model inference requires only 1.4 seconds. To address this bottleneck, we propose a streaming hardware-accelerated feature extraction engine designed for the FPGA programmable logic (PL). The evaluation shows that PL-based streaming engine can reduce feature-extraction latency to the millisecond range. This work positions bitstream-level screening as a first-class primitive and demonstrates that hardware-accelerated preprocessing is the key enabler for securing next-generation reconfigurable custom computing machines at line rate.