Memory-Aware Functional IR for Higher-Level Synthesis of Accelerators
Bibliographic record
Abstract
Specialized accelerators deliver orders of a magnitude of higher performance than general-purpose processors. The ever-changing nature of modern workloads is pushing the adoption of Field Programmable Gate Arrays (FPGAs) as the substrate of choice. However, FPGAs are hard to program directly using Hardware Description Languages (HDLs). Even modern high-level HDLs, e.g., Spatial and Chisel, still require hardware expertise. This article adopts functional programming concepts to provide a hardware-agnostic higher-level programming abstraction. During synthesis, these abstractions are mechanically lowered into a functional Intermediate Representation (IR) that defines a specific hardware design point. This novel IR expresses different forms of parallelism and standard memory features such as asynchronous off-chip memories or synchronous on-chip buffers. Exposing such features at the IR level is essential for achieving high performance. The viability of this approach is demonstrated on two stencil computations and by exploring the optimization space of matrix-matrix multiplication. Starting from a high-level representation for these algorithms, our compiler produces low-level VHSIC Hardware Description Language (VHDL) code automatically. Several design points are evaluated on an Intel Arria 10 FPGA, demonstrating the ability of the IR to exploit different hardware features. This article also shows that the designs produced are competitive with highly tuned OpenCL implementations and outperform hardware-agnostic OpenCL code.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".