Fast and Scalable MAGIC-Based Wallace Tree Multiplier for In-Memory Computing
Bibliographic record
Abstract
The growing demand for high-performance, realtime computation in data-intensive applications is increasingly constrained by the Von Neumann bottleneck. In-memory computing (IMC), particularly through memristor-based technologies such as Memristor-Aided loGIC (MAGIC), offers a promising solution by enabling logic operations directly within memory arrays. While prior research has demonstrated basic Boolean logic with memristors, arithmetic operations such as multiplication remain latency-bound due to sequential logic execution and inefficient crossbar utilization. This work introduces a scalable and efficient MAGIC-based Wallace Tree multiplier architecture tailored for in-memory computing. By integrating an optimized 3:2 compressor and leveraging a state-of-the-art synthesis-tomicro-operation mapping tool, our approach significantly reduces latency and improves parallelism within memristor crossbars. Experimental evaluations across 4- to 64-bit unsigned Wallace Tree multipliers show consistent improvements in speed and scalability. The proposed architecture presents a practical and fully scalable design for next-generation in-memory arithmetic systems.
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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) | 0.003 | 0.001 |
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".