Chainsaw: Von-neumann accelerators to leverage fused instruction chains
Bibliographic record
Abstract
A central tenet behind accelerators is to partition a program execution into regions with different behavior (e.g., SIMD, Irregular, Compute-Intensive) and then use behavior-specialized architectures [1] for each region. It is unclear whether the gains in efficiency arise from recognizing that a simpler microarchitecture is sufficient for the acceleratable code region or the actual microarchitecture, or a combination of both. Many proposals [2], [3] seem to choose dataflow-based accelerators which encounters challenges with fabric utilization and static power when the available instruction parallelism is below the peak operation parallelism available [4]. In this paper, we develop, Chainsaw, a Von-Neumann based accelerator and demonstrate that many of the fundamental overheads (e.g., fetch-decode) can be amortized by adopting the appropriate instruction abstraction. The key insight is the notion of chains, which are compiler fused sequences of instructions. chains adapt to different acceleration behaviors by varying the length of the chains and the types of instructions that are fused into a chain. Chains convey the producer-consumer locality between dependent instructions, which the Chainsaw architecture then captures by temporally scheduling such operations on the same execution unit and uses pipeline registers to forward the values between dependent operations. Chainsaw is a generic multi-lane architecture (4-stage pipeline per lane) and does not require any specialized compound function units; it can be reloaded enabling it to accelerate multiple program paths. We have developed a complete LLVM-based compiler prototype and simulation infrastructure and demonstrated that a 8-lane Chainsaw is within 73% of the performance of an ideal dataflow architecture, while reducing the energy consumption by 45% compared to a 4-way OOO processor.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".