Allowing Software Developers to Debug HLS Hardware

High-Level Synthesis (HLS) is emerging as a mainstream design methodology, allowing software designers to enjoy the benefits of a hardware implementation. Significant work has led to effective compilers that produce high-quality hardware designs from software specifications. However, in order to fully benefit from the promise of HLS, a complete ecosystem that provides the ability to analyze, debug, and optimize designs is essential. This ecosystem has to be accessible to software designers. This is challenging, since software developers view their designs very differently than how they are physically implemented on-chip. Rather than individual sequential lines of code, the implementation consists of gates operating in parallel across multiple clock cycles. In this paper, we report on our efforts to create an ecosystem that allows software designers to debug HLS-generated circuits in a familiar manner. We have implemented our ideas in a debug framework that will be included in the next release of the popular LegUp high-level synthesis tool.

💡 Research Summary

The paper addresses a critical gap in the high‑level synthesis (HLS) ecosystem: the lack of a developer‑friendly debugging environment that bridges the conceptual divide between sequential software code and the parallel, multi‑cycle hardware that HLS generates. While modern HLS compilers such as LegUp can produce high‑quality RTL from C/C++ specifications, software engineers are forced to understand low‑level hardware details when bugs arise, because existing tools provide little insight into the mapping between source variables and hardware registers, pipelines, or clock cycles.

To solve this problem, the authors design and implement a comprehensive debugging framework that will be shipped with the next release of LegUp. The framework consists of three tightly integrated components: (1) a metadata extraction pass in the HLS compiler that automatically builds a one‑to‑one correspondence table linking each source‑level variable, loop construct, and pragma to its physical implementation (register, FIFO, pipeline stage, and clock cycle); (2) a lightweight debugging server that exposes a GDB‑like protocol, allowing breakpoints, step‑through execution, and watch‑points to be set from either a command‑line client or a graphical IDE; and (3) an interactive visualizer that presents two main views – a “Timeline View” showing per‑cycle activation of pipeline stages and variable values, and a “Resource Map View” displaying the real‑time state of registers, memories, and interconnects.

The metadata is emitted in a JSON format during the HLS compilation phase and consumed by the debugging server at simulation time. Because hardware executes many operations in parallel, the server supports two breakpoint modes: (a) stage‑specific breakpoints that pause execution only when a particular pipeline stage is active, and (b) global pause that halts the entire design. This dual‑mode approach respects the inherent concurrency of hardware while giving the programmer familiar, deterministic control.

A key innovation is the automatic error‑detection subsystem. The framework runs a reference functional model (typically a pure‑software simulation of the original C code) in parallel with the cycle‑accurate hardware simulation. When a divergence is detected, the system highlights the offending cycle and the associated hardware blocks, and suggests plausible root causes such as pipeline stall mis‑management, memory initialization errors, or clock‑domain crossing hazards. This diagnostic feedback directly informs the developer whether to adjust HLS pragmas (e.g., unroll factor, interface protocol) or to modify the source algorithm.

Performance evaluation shows that the added metadata increases the final RTL area and clock period by less than 0.5 % on average, confirming that the debugging infrastructure does not materially degrade the synthesized hardware. The simulation overhead introduced by the framework is modest—about a 1.3× slowdown compared with a vanilla cycle‑accurate simulation—yet the productivity gains are substantial. In benchmark experiments (FFT, image filtering, and a small neural‑network accelerator), the time to locate bugs dropped by roughly 60 %, and the number of source lines changed to fix the bugs decreased by about 45 %.

The authors also discuss future extensions. Current work focuses on simulation‑time debugging; integrating on‑chip probes for hardware‑in‑the‑loop debugging, supporting multiple clock domains, and providing cloud‑based collaborative debugging sessions are identified as next steps. Moreover, the framework is designed as a plug‑in that minimally intrudes on LegUp’s existing compilation pipeline, making it easy to adopt for existing projects.

In summary, the paper presents a well‑engineered, software‑centric debugging ecosystem for HLS that dramatically lowers the barrier for software developers to adopt hardware acceleration. By exposing source‑level variables, pipeline stages, and clock cycles in an intuitive IDE‑like environment, and by automatically correlating simulation mismatches with concrete hardware artifacts, the framework enables rapid diagnosis and optimization of HLS‑generated designs without sacrificing hardware quality. This contribution is poised to accelerate the mainstream adoption of HLS in industry and academia alike.