TreeHouse: An MLIR-based Compilation Flow for Real-Time Tree-based Inference

Abstract

TreeHouse presents a novel MLIR-based compilation flow specifically optimized for achieving real-time, low-latency inference of tree-based machine learning models (like Random Forests and GBTs). By leveraging MLIR's retargetability and modular infrastructure, TreeHouse efficiently translates complex, control-flow heavy tree structures into highly optimized code for target hardware. This approach significantly enhances deployment efficiency and performance, making advanced tree models viable for embedded and time-critical applications.

Report

Key Highlights

• MLIR Foundation: TreeHouse utilizes the Multi-Level Intermediate Representation (MLIR) framework, providing a flexible and retargetable compiler architecture specifically adapted for machine learning workloads.
• Targeted Optimization: The primary focus is optimizing the inference phase for tree-based models, addressing the typical performance bottlenecks associated with highly complex control flow and branch prediction failures.
• Real-Time Performance: The system is designed to meet strict latency requirements necessary for deployment in real-time and embedded systems where standard high-level ML frameworks often introduce unacceptable overhead.
• Custom Abstraction: Introduces specialized MLIR dialects to capture the semantics and structure of decision trees more effectively than general-purpose ML representations (like ONNX or standard Linalg), enabling structure-aware optimization.

Technical Details

• Compilation Flow: TreeHouse generally defines a pipeline starting from the trained model (e.g., formats like PMML or specific training library outputs), translating it into a high-level Tree Dialect in MLIR.
• Optimization Passes: Key MLIR passes include techniques for memory layout optimization (transforming recursive tree structures into flat, cache-friendly array representations), branch merging, path compression, and aggressive constant propagation based on the static structure of the trees.
• Hardware Mapping: The optimized representation is lowered through intermediate MLIR dialects (like the Standard Control Flow and potentially Linalg dialects) down to LLVM IR, ensuring efficient code generation for various CPU backends.
• Performance Metrics: The resulting code typically shows substantial gains in throughput and reductions in latency compared to running the same models through generic runtime environments, emphasizing reduced instruction counts and improved memory access patterns.

Implications

• RISC-V Ecosystem Enablement: TreeHouse is highly relevant for the RISC-V community, as MLIR’s design inherently supports retargeting to diverse instruction sets. This allows the framework to efficiently compile high-performance tree inference code for the wide array of RISC-V processors, from small microcontrollers to high-end application cores.
• Embedded ML Acceleration: Tree-based models are often favored in safety-critical and resource-constrained embedded environments (e.g., automotive, industrial control) due to their transparency and moderate footprint. TreeHouse makes the deployment of complex, highly accurate ensemble models (like GBTs) more practical on RISC-V hardware by maximizing inference speed.
• Leveraging RISC-V Extensions: The compiler flow can be customized to exploit specific RISC-V instruction set extensions. For example, optimizations related to feature fetching or small data operations could potentially leverage the P (Packed SIMD) extension or ensure memory accesses are optimized for specific memory hierarchies common in RISC-V SoCs.
• Standardization of ML Toolchains: By adopting MLIR, TreeHouse contributes to the growing ecosystem of standardized, modular ML compilation infrastructure, moving away from fragmented, hardware-specific solutions and accelerating the adoption of RISC-V in the AI/ML sector.