An Energy-Efficient Binary-Interfaced Stochastic Multiplier Using Parallel Datapaths
Bibliographic record
Abstract
Stochastic computing (SC) typically requires a low design complexity compared with weighted binary computing, so it has been successfully applied in neural networks (NNs). Usually, SC utilizes random bitstreams as its medium, which makes it suffer from a long delay that offsets its advantages. This drawback can be alleviated by utilizing parallel datapaths, which, however, will significantly increase the hardware cost due to the requirement of multiple parallel computing units. In this article, a hybrid bit-splitting generator (HBSG) is proposed to efficiently produce parallel bitstreams in a single clock cycle to reduce delay. The HBSG uniformly splits binary numbers into R segments, each of which is encoded in parallel by using hardwired connections according to the weight of each bit. A binary-interfaced parallel stochastic multiplier (BipSMul) using the HBSG is then proposed to accelerate the multiplication in SC. Experimental results show that the BipSMul is more energy efficient than the state-of-the-art parallel and serial stochastic designs, as well as their binary and Booth counterparts, in delay, power-delay product (PDP), and area-delay product (ADP).
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".