Compiler DAG Scheduling for ML Model Execution Delay
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Solution Overview
Problem
Current methods for implementing machine learning models in embedded systems fail to guarantee real-time processing and adhere to strict power and resource constraints, particularly in applications like self-driving cars.
Innovation Solution
A compiler system that generates a directed acyclic graph (DAG) of a machine learning model, rewrites operators into special purpose hardware instructions, schedules these instructions down to each clock cycle, optimizes them within desired runtime constraints, and assembles them into a binary for execution on a special purpose processor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a GPU is used to execute machine learning models in an embedded system, then processing speed may be improved, but time constraints and power consumption limits are violated
Solution Approach 1:
The compiler performs preliminary scheduling of instructions during the compilation phase, determining the exact execution time of each instruction before runtime. This static scheduling ensures that the model execution completes within the required time constraint by pre-calculating the execution timeline and inserting appropriate delay instructions, rather than relying on dynamic scheduling that cannot guarantee timing.
Solution Approach 2:
The system changes the execution parameters by translating the machine learning model into a directed acyclic graph (DAG) representation and then into a statically scheduled instruction sequence with explicit timing information. This transformation from a high-level model to a low-level timed instruction sequence changes the execution parameters to ensure deterministic timing behavior that meets real-time constraints.
2Productivity
If a GPU is used to execute machine learning models, then processing capability is improved, but power consumption exceeds platform capabilities
Solution Approach 1:
The compiler performs preliminary power analysis during compilation by analyzing the DAG and calculating the total power consumption of the scheduled instruction sequence. This allows the system to determine upfront whether the execution will exceed power constraints and take corrective actions such as inserting delays or optimizing the instruction schedule before runtime, ensuring power consumption remains within platform capabilities.
Solution Approach 2:
The system introduces periodic idle cycles or delay instructions into the execution schedule to regulate power consumption. By inserting NOP (no operation) instructions at strategically determined positions in the instruction sequence, the system creates periodic low-power intervals that reduce average power consumption while maintaining the required processing capability within the power envelope.
3Ease of operation
If instructions are scheduled without considering dependencies, then scheduling simplicity is improved, but execution delay increases
Solution Approach 1:
The system segments the machine learning model into a directed acyclic graph where operations are divided into discrete nodes and edges representing data dependencies. This segmentation allows the compiler to systematically analyze dependencies between operations and schedule them in an optimal order, reducing execution delay while maintaining manageable complexity through the structured DAG representation.
Solution Approach 2:
The compiler performs feedback analysis by examining the DAG to identify dependency relationships between instructions. This feedback information about data flows and dependencies is used to adjust the scheduling order, ensuring that instructions are executed in an optimal sequence that minimizes idle time and pipeline stalls while still being systematically determinable during compilation.
Data Source
AI summary
A system receives a predictive model and receives one or more runtime constraints. The system generates a directed acyclic graph (DAG) of the predictive model indicating dependencies. The system compiles the predictive model into first instructions for a first processor based on the one or more runtime constraints and the DAG. The system packages first instructions, the one or more runtime constraints, and the DAG of the predictive model in a first binary. The system recompiles the predictive model into second instructions for a second processor based on the runtime constraints and the DAG stored in the first processor. The system packages the second instructions, the DAG, and the runtime constraints in a second binary.