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
With Dennard scaling ending, architects are turning to domain-specific accelerators (DSAs). State-of-the-art DSAs work with sparse data [37] and indirectly-indexed data structures [18, 30]. They introduce non-affine and dynamic memory accesses [7, 35], and require domain-specific caches. Unfortunately, cache controllers are notorious for being difficult to architect; domain-specialization compounds the problem. DSA caches need to support custom tags, data-structure walks, multiple refills, and preloading. Prior DSAs include ad-hoc cache structures, and do not implement the cache controller. We propose X-Cache, a reusable caching idiom for DSAs. We will be open-sourcing a toolchain for both generating the RTL and programming X-Cache. There are three key ideas: i) DSA-specific Tags (Meta-tag): The designer can use any combination of fields from the DSA-metadata as the tag. Meta-tags eliminate the overhead of walking and translating metadata to global addresses. This saves energy, and improves load-to-use latency. ii) DSA-programmable walkers (X-Actions): We find that a common set of microcode actions can be used to implement the DSA-specific walking, data block, and tag management. We develop a programmable microcode engine that can efficiently realize the data orchestration. iii) DSA-portable controller (X-Routines): We use a portable abstraction, coroutines, to let the designer express walking and orchestration. Coroutines capture the block-level parallelism, remain lightweight, and minimize controller occupancy. We create caches for four different DSA families: Sparse GEMM [35, 37], GraphPulse [30], DASX [22], and Widx [18]. X-Cache outperforms address-based caches by 1.7 × and remains competitive with hardwired DSAs (even 50% improvement in one case). We demonstrate that meta-tags save 26--79% energy compared to address-tags. In X-Cache, meta-tags consume 1.5--6.5% of data RAM energy and the programmable microcode adds a further 7%.
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