Energy-Efficient Hybrid STT-MTJ/CMOS Circuit for Machine Learning-Assisted Neuromorphic Computing Applications
Bibliographic record
Abstract
The CMOS-based neuromorphic computing system (NCS) face significant challenges, such as increasing energy usage and vast area footprints, surpassing the efficiency of biological brains. Spin transfer torque magnetic tunnel junction (STT-MTJ), a type of spin device@comm offers convenient benefits, including nonvolatility, increased energy efficiency, increased speed of operation, and compatibility with CMOS, making them ideal for energy-efficient spiking NCSs that exhibit neuronal behavior. However, the high energy consumption in spintronic-based NCS, primarily due to the high write current required for MTJ switching, remains a significant challenge as neurons in these systems tend to stay active longer than necessary. To address this challenge, we introduce a novel hybrid STT-MTJ/CMOS write terminate circuit (SM-WTC) that efficiently terminates the MTJ current efficiently after MTJ-state switches, significantly improving energy consumption and speed by 2.6× and 2.3×, compared to conventional NCSs. The proposed SM-WTC technique achieves energy consumption reductions of 52.7%, 58.3%, and 62.18% compared to prior work in real-time sensing (RTS) circuit, common-mode tracking and terminating circuit (CM-TTC), and conventional-NCS, respectively. A Cadence Virtuoso simulation using 65-nm CMOS technology has been used to evaluate the proposed circuit. Furthermore, SM-WTC-based NCS achieves a 67.2% improvement in energy-delay product (EDP) over conventional NCS for image edge detection. These advancements position SM-WTC as a commercially viable solution for next-generation artificial intelligence (AI) accelerators and brain-inspired computing architecture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".