A Data-Driven Dynamic Execution Orchestration Architecture

Domain-specific accelerators deliver exceptional performance on their target workloads through fabrication-time orchestrated datapaths. However, such specialized architectures often exhibit performance fragility when exposed to new kernels or irregular input patterns. In contrast, programmable architectures like FPGAs, CGRAs, and GPUs rely on compiletime orchestration to support a broader range of applications; but they are typically less efficient under irregular or sparse data. Pushing the boundaries of programmable architectures requires designs that can achieve efficiency and high-performance on par with specialized accelerators while retaining the agility of general-purpose architectures.

We introduce Canon, a parallel architecture that bridges the gap between specialized and general purpose architectures. Canon exploits data-level and instruction-level parallelism through its novel design. First, it employs a novel dynamic data-driven orchestration mechanism using programmable Finite State Machines (FSMs). These FSMs are programmed at compile time to encode high-level dataflow per state and translate incoming meta-information (e.g., sparse coordinates) into control instructions at runtime. Second,