FinFET 6T-SRAM Compute-in-Memory Targeting Low Power Neural Networks Operations
Bibliographic record
Abstract
Modern computing relies on the artificial intelligence (AI) for making independent decisions. AI utilizes neural networks (NN) to handle complex tasks execution such as image recognition, text processing, and language interpretation. However, NNs are extremely data centric and pose performance bottleneck while moving data between memory and processing unit. Compute-in-memory (CIM) addresses this issue but limited attention has been given to the low power neural network operations. This paper proposes a novel CIM solution for multiply and accumulate (MAC) operations without incorporating any dedicated arithmetic units. Proposed CIM makes use of FinFET 6T-SRAM cells, sense amplifiers, write drivers, and NOR gates to achieve the power efficient in-memory computations. Simulations in 12nm FinFET shows that proposed CIM for 2-bit input and 2-bit weights: consumes$411.2\mu \mathrm{W}$, exhibits latency of 36.1 ns, and shows maximum power efficiency of 6.51 KOP/W.
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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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".