Reducing the Performance Gap between Soft Scalar CPUs and Custom Hardware with TILT
Bibliographic record
Abstract
By using resource sharing field-programmable gate array (FPGA) compute engines, we can reduce the performance gap between soft scalar CPUs and resource-intensive custom datapath designs. This article demonstrates that Thread- and Instruction-Level parallel Template architecture (TILT), a programmable FPGA-based horizontally microcoded compute engine designed to highly utilize floating point (FP) functional units (FUs), can improve significantly the average throughput of eight FP-intensive applications compared to a soft scalar CPU (similar to a FP-extended Nios). For eight benchmark applications, we show that: (i) a base TILT configuration having a single instance for each FU type can improve the performance over a soft scalar CPU by 15.8 × , while requiring on average 26% of the custom datapaths’ area; (ii) selectively increasing the number of FUs can more than double TILT’s average throughput, reducing the custom-datapath-throughput-gap from 576 × to 14 × ; and (iii) replicated instances of the most computationally dense TILT configuration that fit within the area of each custom datapath design can reduce the gap to 8.27 × , while replicated instances of application-tuned configurations of TILT can reduce the custom-datapath-throughput-gap to an average of 5.22 × , and up to 3.41 × for the Matrix Multiply benchmark. Last, we present methods for design space reduction, and we correctly predict the computationally densest design for seven out of eight benchmarks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".