Dynamic Instruction Graph Generation for Hardware Accelerators

The patent implements dynamic instruction generation by transforming a static computational graph into multiple candidate instruction sequences based on hardware capabilities. The system dynamically selects and optimizes instruction sequences at runtime or compile time, allowing the same computational algorithm to be efficiently executed on different hardware accelerators with varying capabilities without requiring manual reconfiguration of the underlying computation graph.

The patent segments the computational graph into independent operations and subgraphs that can be separately mapped to different hardware instructions. By dividing the computational task into discrete, mappable units, the system can selectively optimize different portions of the computation for specific hardware capabilities while maintaining overall adaptability across diverse accelerator architectures.

The patent performs preliminary analysis of hardware capabilities and computational graph characteristics before generating optimized instruction sequences. By pre-computing compatibility mappings between operations and hardware features, and pre-identifying optimization opportunities in the computational graph, the system reduces the time required for instruction generation while maintaining high execution efficiency on target hardware accelerators.

The patent implements self-service optimization where the system automatically analyzes its own computational graphs and generates optimized instruction sequences without requiring manual intervention. The instruction generation system serves itself by using the computational graph structure and hardware capability descriptions to autonomously produce optimized code, eliminating the need for manual optimization efforts while maintaining high productivity.