A High-Performance Stochastic Simulated Bifurcation Ising Machine
Bibliographic record
Abstract
Ising model-based computers, or Ising machines, have recently emerged as high-performance solvers for combinatorial optimization problems (COPs). A simulated bifurcation (SB) Ising machine searches for the solution by solving pairs of differential equations related to the oscillator positions and momenta. It benefits from massive parallelism but suffers from high energy. As an unconventional computing paradigm, dynamic stochastic computing implements accumulation-based operations efficiently. By exploiting the advantages in algorithm and hardware codesign, this article proposes a high-performance stochastic SB machine (SSBM) with efficient hardware. To this end, we develop a stochastic SB (sSB) algorithm such that the multiply-and-accumulate (MAC) operation is converted to multiplexing and addition while the numerical integration is implemented by using signed stochastic integrators (SSIs). Specifically, the sSB stochastically ternarizes position values used for the MAC operation. Two types of SB cells are constructed. A stochastic computing SB cell contains two SSIs with a high area efficiency, while a binary-stochastic computing SB cell contains one binary integrator and one SSI with a reduced delay. Based on sSB, an SSBM is then built by using the proposed SB cells as the basic building block. The designs and syntheses of two SSBMs with 2000 fully connected spins require at least 10.62% smaller area than the state-of-the-art designs. It shows the potential of stochastic computing for SB to efficiently solve COPs.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".