A Survey of Analog Computing for Domain-Specific Accelerators
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
Analog computing has re-emerged as a powerful tool for solving complex problems in various domains due to its energy efficiency and inherent parallelism. This paper summarizes recent advancements in analog computing, exploring discrete time and continuous time methods for solving combinatorial optimization problems, solving partial differential equations and systems of linear equations, accelerating machine learning (ML) inference, multi-beam beamforming, signal processing, quantum simulation, and statistical inference. We highlight CMOS implementations that leverage switched-capacitor, switched-current, and radio-frequency circuits, as well as non-CMOS implementations that leverage non-volatile memory, wave physics, and stochastic processes. These advancements demonstrate high-speed, energy-efficient computations for computational electromagnetics, finite-difference time-domain (FDTD) solvers, artificial intelligence (AI) inference engines, wireless systems, and related applications. Theoretical foundations, experimental validations, and potential future applications in high-performance computing and signal processing are also discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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