Development of Compute-in-Memory Memristive Crossbar Architecture with Composite Memory Cells
Bibliographic record
Abstract
In this chapter, we discuss the compute-in-memory memristive architectures and develop a 2M1M crossbar array which can be applied for both memory and logic applications. In the first section of this chapter, we briefly discuss compute-in-memory memristive architectural concepts and specifically investigate the current state off the art composite memristor-based switch cells. Also, we define their applications e.g. digital/analog logic, memory, etc. along with their drawbacks and implementation limitations. These composite cells can be designed to be adapted into different design needs can enhance the performance of the memristor crossbar array while preserving their advantages in terms of area and/or energy efficiency. In the second section of the chapter, we discuss a 2M1M memristor switch and its functionality which can be applied into memory crossbars and enables both memory and logic functions. In the next section of the chapter, we define logic implementation by using 2M1M cells and describe variety of in-memory digital logic 2M1M gates. In the next section of the chapter, 2M1M crossbar array performance to be utilized as memory platform is described and we conceived pure memristive 2M1M crossbar array maintains high density, energy efficiency and low read and write time in comparison with other state of art memory architectures. This chapter concluded that utilizing a composite memory cell based on non-volatile memristor devices allow a more efficient combination of processing and storage architectures (compute-in-memory) to overcome the memory wall problem and enhance the computational efficiency for beyond Von-Neumann computing platforms.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".