Low power design techniques have been extensively applied in modern IC designs to avoid negative side effects from high power density. Unlike Dynamic Voltage and/or Frequency Scaling (DVFS) approaches only applied on a “fixed” design, we propose a dynamic logic reconfigurable structure strategy which allows dynamic switching from a high speed/power logic structure to a low speed/power logic structure. A design with such configurable structure is called Dynamic Logic Reconfigurable Structure (DLRS). Different from approximate computing which trades off between computation accuracy and power, our DLRS designs maintain data integrity. In this paper, we propose novel low-cost DLRS adders and multipliers, and a comprehensive framework for low power designs. We further integrate DLRS with DVFS, which creates more flexibility to trade-off between performance and power consumption. Experimental results show that with DLRS adders and multipliers in three indoor designs, the proposed method can achieve up to 60.05% power reduction compared with traditional DVFS scheme with only 6.55% area overhead.

Dynamic voltage scaling (DVS) has been widely used to suppress power consumption in modern designs. The decision of optimal operating voltage at runtime should consider the variations in workload, process as well as environment. As these variations are hard to predict accurately at design time, various reinforcement learning based DVS schemes have been proposed in the literature. However, none of them can be readily applied to designs with graceful degradation, where timing errors are allowed with bounded probability to trade for further power reduction. In this paper, we propose a Q-learning based DVS scheme dedicated to the designs with graceful degradation. We compare it with two deterministic DVS schemes, i.e., a stepping based scheme and a statistical modeling based scheme. Experimental results on three 45nm industrial designs show that the proposed Q-learning based scheme can achieve up to 83.9% and 29.1% power reduction respectively with 0.01 timing error probability bound. To the best of the authors' knowledge, this is the first in-depth work to explore reinforcement learning based DVS schemes for designs with graceful degradation.

In 3-D integrated circuits, through silicon via (TSV) is a critical enabling technique to provide vertical connections. However, it may suffer from many reliability issues such as undercut, misalignment, or random open defects. Various fault-tolerance mechanisms have been proposed in literature to improve yield, at the cost of significant area overhead. In this paper, we focus on the structure that uses one spare TSV for a group of original TSVs, and study the optimal assignment of spare TSVs under yield and timing constraints to minimize the total area overhead. We show that such problem can be modeled as a constrained graph decomposition problem. Two efficient heuristics are further developed to address this problem. Experimental results show that under the same yield and timing constraints, our heuristic can reduce the area overhead induced by the fault-tolerance mechanisms by up to 61%, compared with a seemingly more intuitive nearest-neighbor-based heuristic.

Sub-threshold designs play an important role in energy-constrained applications. In those designs, path delays depend exponentially on threshold voltage/temperature. As such, dynamic configurations at runtime are desired for best trade-off between operating power and performance. Unfortunately, most existing works only consider either process or temperature variations but not both, resulting in sub-optimal configurations or even functional failures. Moreover, little study has been performed on the graceful degradation of sub-threshold designs, which is important in the presence of drastic delay variations. Towards this, we present a novel critical path monitor based dynamic voltage scaling scheme. Considering both process and temperature variations, it minimizes the operating power under a given timing error probability (TEP) bound. An exact method to decide the optimal switching thresholds is also proposed. Experimental results on 45nm industrial designs show that with only 1% TEP, our scheme can reduce the operating power by up to 75.3% compared with the constant voltage scheme. To the best of the authors' knowledge, this is the very first work on dynamic configuration for graceful degradation in sub-threshold designs.

In advanced IC manufacturing, as the gap increases between lithography optical wavelength and feature size, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which extracts appropriate feature vectors of hotspot and nonhotspot patterns. Our model can dynamically tune appropriate fuzzy regions around known hotspots. Based on this paper, we develop a fast algorithm for lithography hotspot detection with high accuracy of detection and low probability of false-alarm counts. In addition, since higher dimensional size of feature vectors can produce better accuracy but requires longer run time, this paper proposes a grid reduction technique to significantly reduce the CPU run time with very minor impact on the advantages of higher dimensional space. Our results are very encouraging, with average 94.5% accuracy and low false-alarm counts on a set of test benchmarks.

In advanced IC manufacturing, as the gap between lithography optical wavelength and feature size increases, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which can dynamically tune appropriate fuzzy regions around known hotspots. Based on this model, we develop a fast algorithm for lithography hotspot detection with very low chances of false-alarm. Our results are very encouraging with under 0.56 CPU-hrs/mm2 runtime.