Runtime Verification on FPGAs with LTLf Specifications

Runtime verification is a technique that evaluates a system's execution trace at runtime against a formal specification. This approach is particularly useful for safety-critical and autonomous systems to verify system functionality and allow for graceful recovery or intervention in the case of system faults. Specifications are often provided in a high-level form using some type of temporal logic, which can then be compiled into an automaton to be used as a monitor for the system. Existing work has mainly focused on implementing such monitors in software. In recent years there has been extensive research, however, in hardware acceleration of automata applications, which can potentially be extended to runtime monitoring. In this paper, we introduce an open-source framework for translating formulas in Linear Temporal Logic over finite traces (LTLf) into automata implementations on FPGAs for high-efficiency and high-performance runtime monitoring. By using the spatial dimension of FPGAs, we run many of these automata in parallel, significantly reducing the latency between violation and monitor report and achieving significant throughput. We compare the performance of four different architectures corresponding to the combinations of deterministic or nondeterministic automata with an explicit or symbolic representation, and determine the design parameters that result in efficient hardware utilization and higher clock frequencies. We found that explicit automata tend to use more hardware resources, in particular Lookup Tables (LUTs), than symbolic automata. An exception to this is in the case of Flip-Flop (FF) usage, where symbolic DFAs tend to use more FF resources than explicit NFAs for smaller designs. We also found that explicit NFAs can run at higher clock frequencies, except for very large automata with high edge densities. Symbolic NFAs use fewer Look-Up Table resources and run at higher clock frequencies than symbolic DFAs, whereas symbolic DFAs required fewer Flip-Flop resources, except in the case of very simple small automata with lower edge densities. Finally, we found that explicit automata hardware utilization significantly increases with input signal widths, motivating the use of symbolic automata for wide input signals.