Chainsaw: Von-neumann accelerators to leverage fused instruction chains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it