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 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 | 0.000 |
| 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".