- The paper demonstrates that automated reasoning and formal hardware models can significantly enhance compiler backend generation for optimized and correct system performance.
- Evaluation shows that the Lakeroad tool outperforms traditional methods in accelerator offloading and FPGA DSP mapping, as confirmed by comprehensive benchmarks.
- The work validates scalable, architecture-independent techniques that reduce development time and support advanced hardware-software co-design.
The dissertation "Generation of Compiler Backends from Formal Models of Hardware" by Gus Henry Smith explores the automatic generation of compiler backends leveraging explicit, formal models of hardware. The work posits that automated reasoning algorithms and formal hardware models can significantly optimize compiler backend generation, leading to improved correctness, optimization opportunities, and reduced development time.
Motivation and Background
The dissertation spans two primary case studies with distinct domains: machine learning accelerators and FPGA technology mapping. The unifying premise of both studies is to address the inefficiencies and complexities associated with traditional compiler construction methods, which often miss optimization opportunities and are difficult to extend for new hardware.
Compilation to Machine Learning Accelerators
The first part of the dissertation introduces Lakeroad, a pure tensor program IR designed to facilitate the application of equality saturation for hardware-level term rewriting. This IR separates computation from data access patterns and supports flexible matching for accelerator offloading by implementing syntactic rewrites capturing the functional behavior of accelerators using the term-rewriting algorithm.
Lakeroad's integration into the 3LA framework demonstrated notable improvements. Flexible matching identified more opportunities for invoking accelerators, resulting in higher optimization efficacy. This was evaluated through various benchmarks, where Lakeroad-based flexible matching outperformed the syntactic pattern matching approach in BYOC, identifying in many cases significantly more offloads to accelerators.
Compilation to FPGAs
In the second part of the dissertation, focus shifts to FPGA technology mapping with the introduction of another tool named Lakeroad (distinct from the previously mentioned tool). This Lakeroad tool employs sketch-guided program synthesis, a powerful automated reasoning algorithm, to automatically generate DSP mappings from vendor-provided HDL simulation models. The approach aims to enhance completeness, correctness, and extensibility of the technology mapping process.
Key contributions in this domain include:
- Program Synthesis: Utilizing formal hardware models extracted automatically from HDL simulations ensures that the generated compiler backends are both correct by construction and able to find more complete optimizations.
- Sketch Templates: Architecture-independent sketch templates enable the reuse of common FPGA implementation patterns across different architectures, reducing the need for extensive manual intervention.
- Evaluation: Comprehensive evaluation against existing FPGA technology mapping tools demonstrated that Lakeroad could map significantly more designs to a single DSP, showcasing superior completeness.
Evaluation and Results
The dissertation's formal and empirical evaluations underscore the advantages of the proposed approach. For machine learning accelerators, the 3LA flow, integrated with Lakeroad, provided higher-performance offloads and enhanced hardware validation through more effective end-to-end simulation capabilities. For FPGA technology mapping, Lakeroad outperformed both Yosys and proprietary state-of-the-art tools across various benchmarks, confirming the tool's stronger completeness and efficiency in DSP mapping.
Implications and Future Work
This research has practical implications for the future of compiler backend generation. The automatic generation techniques endorsed by Lakeroad can be expanded to other domains requiring hardware-software co-design, scaling to support more complex hardware primitives and more sophisticated hardware behaviors such as timing and power models.
The work also paves the way for novel uses of automated reasoning in hardware design, such as decompilation of lower-level hardware descriptions back to higher-level representations, potentially aiding in hardware verification and migration tasks.
Conclusion
The dissertation "Generation of Compiler Backends from Formal Models of Hardware" by Gus Henry Smith presents a compelling case for leveraging automated reasoning algorithms with explicit hardware models to automatically generate robust, efficient compiler backends. The two comprehensive case studies validate the hypothesis that this approach can lead to substantial improvements in optimization capability, correctness guarantees, and reduced development time in both machine learning accelerators and FPGA technology mapping domains. The methodologies and tools introduced in this work, specifically the Lakeroad tools, represent significant strides forward in automated compiler backend generation.