Bibliographic record
Abstract
Leakage power is emerging as a key challenge in IC design. Leakage is increasingly exponentially with each technology generation and is expected to become the dominant part of total power. Device threshold voltage scaling, shrinking device dimensions, and larger circuit sizes are causing this dramatic increase in leakage. As leakage varies exponentially with process parameters, yield of the chip is often directly influenced by leakage. Increasing amount of leakage is also critical for power constraint ICs. Traditionally, leakage has been considered as an important design variable in handheld devices and in standby circuit operation. However, this significant increase of leakage now warrants that it be considered as the key design variable in all IC designs.This tutorial presents a comprehensive review of leakage power issues in IC design. The tutorial is organized in four major parts. The first part provides an overview of technology and scaling trends which are causing the significant increase in leakage current. The device physics that leads to sub-threshold and gate leakage will be described, along with their dependence on circuit design variables. This part of the tutorial will also cover basic transistor and circuit techniques to minimize leakage, such as the stack effect.The second part of the tutorial will focus on circuit level leakage estimation and avoidance. Use of multiple threshold voltages has been very successful in controlling the leakage of the circuit. Comprehensive description of multiple-Vt techniques for leakage avoidance will be presented along with associated leakage estimation techniques. Multiple-threshold design (MTCMOS) will be described along with its leakage benefits and performance trade-offs. Multiple oxide technology options and associated impact on gate leakage will also be discussed.Third part of the tutorial focuses on chip level effects on leakage. Leakage is heavily dependent on local and global process variations and can vary by an order of magnitude over the technology spread. Leakage estimation techniques which consider both inter and intra-die process variations will be covered. This part of the tutorial also focuses on chip-level leakage minimization techniques. Leakage minimization techniques such as Adaptive Body Bias (ABB) and power supply control will be presented.The last part of the tutorial covers system and circuit architectures for leakage avoidance. In standby mode, the leakage of the circuit can be lowered by putting it a low-leakage state. Caches and memory circuits occupy large percentage of area in model chips. The leakage of caches and memories need to be carefully controlled. This section of the tutorial will cover topics including state assignment for leakage minimization, leakage-driven memory and cache circuits and architectures.The tutorial is intended for designers and CAD engineers interested in next generation design techniques and methodologies and emerging power challenges. Basic background of VLSI and CAD is useful though not needed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.117 | 0.073 |
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".