A review on computational storage devices and near memory computing for high performance applications
Bibliographic record
Abstract
The von Neumann bottleneck is imposed due to the explosion of data transfers and emerging data-intensive applications in heterogeneous system architectures. The conventional computation approach of transferring data to CPU is no longer suitable especially with the cost it imposes. Given the increasing storage capacities, moving extensive data volumes between storage and computation cannot scale up. Hence, high-performance data processing mechanisms are needed, which may be achieved by bringing computation closer to data. Gathering insights where data is stored helps deal with energy efficiency, low latency, as well as security. Storage bus bandwidth is also saved when only computation results are delivered to the host memory. Various applications, including database acceleration, machine learning, Artificial Intelligence (AI), offloading (compression/encryption/encoding) and others can perform better and become more scalable if the “move process to data” paradigm is applied. Embedding processing engines inside Solid-State Drives (SSDs), transforming them to Computational Storage Devices (CSDs), provides the needed data processing solution. In this paper, we review the prior art on Near Data Processing (NDP) with focus on In-Storage Computing (ISC), identifying main challenges and potential gaps for future research directions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".