Machine Learning for Microcontroller Performance Screening

In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of Fmax (the maximum operating frequency). Traditional speed-binning techniques are not feasible to be applied to mass production, due to the high cost of the needed test equipment. Literature has proven that data extracted from on-chip ring oscillators (ROs) can model the Fmax of integrated circuits by means of machine learning models able to predict the actual operating frequency of the devices. Those models, once trained, can be easily applied to the ROs data coming from every produced device with low effort and no need for high-cost equipment. This research aims to develop machine learning methodologies to be deployed in the MCU screening process, allowing for a more efficient and accurate Fmax estimation, as well as improved speed binning. The effectiveness of this approach has been demonstrated on a real world dataset of microcontroller data

Pismo Paolu Antoniu Foscariniju

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Semi-Supervised Deep Learning for Microcontroller Performance Screening

In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of F_max (the maximum operating frequency). Data extracted from on-chip ring oscillators (ROs) can model the F_max of integrated circuits using machine learning models. Those models are suitable for the performance screening process. Acquiring data from the ROs is a fast process that leads to many unlabeled data. Contrarily, the labeling phase (i.e., acquiring F_max) is a time-consuming and costly task, that leads to a small set of labeled data. This paper presents deep-learning-based methodologies to cope with the low number of labeled data in microcontroller performance screening. We propose a method that takes advantage of the high number of unlabeled samples in a semi-supervised learning fashion. We derive deep feature extractor models that project data into higher dimensional spaces and use the data feature embedding to face the performance prediction problem with simple linear regression. Experiments showed that the proposed models outperformed state-of-the-art methodologies in terms of prediction error and permitted us to use a significantly smaller number of devices to be characterized, thus reducing the time needed to build ML models by a factor of six with respect to baseline approaches

A Multi-Label Active Learning Framework for Microcontroller Performance Screening

In safety-critical applications, microcontrollers have to be tested to satisfy strict quality and performances constraints. It has been demonstrated that on-chip ring oscillators can be be used as speed monitors to reliably predict the performances. However, any machine-learning model is likely to be inaccurate if trained on an inadequate dataset, and labeling data for training is quite a costly process. In this paper, we present a methodology based on active learning to select the best samples to be included in the training set, significantly reducing the time and cost required. Moreover, since different speed measurements are available, we designed a multi-label technique to take advantage of their correlations. Experimental results demonstrate that the approach halves the training-set size, with respect to a random labelling, while it increases the predictive accuracy, with respect to standard single-label machine-learning models

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SuperSAGE: the drought stress-responsive transcriptome of chickpea roots

Background Drought is the major constraint to increase yield in chickpea (Cicer arietinum). Improving drought tolerance is therefore of outmost importance for breeding. However, the complexity of the trait allowed only marginal progress. A solution to the current stagnation is expected from innovative molecular tools such as transcriptome analyses providing insight into stress-related gene activity, which combined with molecular markers and expression (e)QTL mapping, may accelerate knowledge-based breeding. SuperSAGE, an improved version of the serial analysis of gene expression (SAGE) technique, generating genome-wide, high-quality transcription profiles from any eukaryote, has been employed in the present study. The method produces 26 bp long fragments (26 bp tags) from defined positions in cDNAs, providing sufficient sequence information to unambiguously characterize the mRNAs. Further, SuperSAGE tags may be immediately used to produce microarrays and probes for real-time-PCR, thereby overcoming the lack of genomic tools in non-model organisms. Results We applied SuperSAGE to the analysis of gene expression in chickpea roots in response to drought. To this end, we sequenced 80,238 26 bp tags representing 17,493 unique transcripts (UniTags) from drought-stressed and non-stressed control roots. A total of 7,532 (43%) UniTags were more than 2.7-fold differentially expressed, and 880 (5.0%) were regulated more than 8-fold upon stress. Their large size enabled the unambiguous annotation of 3,858 (22%) UniTags to genes or proteins in public data bases and thus to stress-response processes. We designed a microarray carrying 3,000 of these 26 bp tags. The chip data confirmed 79% of the tag-based results, whereas RT-PCR confirmed the SuperSAGE data in all cases. Conclusion This study represents the most comprehensive analysis of the drought-response transcriptome of chickpea available to date. It demonstrates that – inter alias – signal transduction, transcription regulation, osmolyte accumulation, and ROS scavenging undergo strong transcriptional remodelling in chickpea roots already 6 h after drought stress. Certain transcript isoforms characterizing these processes are potential targets for breeding for drought tolerance. We demonstrate that these can be easily accessed by micro-arrays and RT-PCR assays readily produced downstream of SuperSAGE. Our study proves that SuperSAGE owns potential for molecular breeding also in non-model crops

Aspects of leaf anatomy of tropical kudzu related to water and energy balance

No Brasil, a puerária (Pueraria phaseoloides Benth, Leguminosae-Faboideae) é tradicionalmente empregada como cultura de cobertura em seringais na região amazônica, onde as condições climáticas se assemelham às condições climáticas tropicais encontradas no sudeste asiático, centro de dispersão da espécie. Puerária foi também introduzida na região sudeste do Brasil, região esta caracterizada pela transição entre os climas tropical e subtropical. Este trabalho teve como objetivo descrever algumas das características da anatomia foliar de puerária relacionadas á economia de água e energia. A epiderme inferior apresentou maior frequência de estômatos (213 estômatos.mm-2) quando comparada à epiderme superior (101 estômatos.mm-2). O número médio de tricomas por milímetro quadrado foi 9 para a epiderme superior e 13 para a epiderme inferior. O comprimento médio dos tricomas foi de 300 mm para a epiderme superior e 460 mm para a epiderme inferior. A espessura da cutícula não diferiu significativamente entre as epidermes inferior e superior. A lâmina foliar consistiu basicamente de duas camadas de ambos, parênquima paliçádico e parênquima lacunoso. Uma camada de mesófilo paranerval foi encontrada entre os parênquimas paliçádico e lacunoso.Tropical kudzu (Pueraria phaseoloides Benth, Leguminosae-Faboideae) has been established in southeastern Brazil in a region characterized by the transition between subtropical and tropical biomes. The seasonal changes in temperature and water availability found in this region are very distinct from those found in the region where tropical kudzu is native. The objective of this paper was to describe characteristics of leaflet anatomy related to water and energy balance in tropical kudzu. The lower epidermis of tropical kudzu showed a higher frequency of stomata (213 stomata.mm-2) than the upper epidermis (101 stomata.mm-2). Trichomes were present in both lower and upper epidermis. The average number of trichomes per square millimeter was 9 for the upper epidermis and 13 for the lower epidermis. The average trichome length was 300 mm for the upper epidermis and 460 mm for the lower epidermis. Cuticle thickness was not considerably different between lower and upper epidermis. The leaflet blade consisted basically of two layers (upper and lower) of unicellular epidermis and two layers of both palisade and spongy parenchyma. One layer of paraveinal mesophyll was found between palisade and spongy parenchyma

Aspectos da anatomia foliar de Pueraria lobata (Leguminosae-Faboideae) associados ao balanço de água e de energia

Kudzu is a cover crop that has escaped cultivation in some subtropical and warm temperate regions. Kudzu has previously demonstrated broad intraspecific physiological plasticity while colonizing new environments. The objective of this paper was to investigate characteristics of kudzu leaflet anatomy that might contribute to its successful growth in climatically distinct environments, and to escape cultivation as well. Fresh and fixed leaflet strips of field-grown plants were analyzed. The lower epidermis of kudzu showed a higher frequency of stomata (147 ± 19 stomata mm-2) than the upper epidermis (26 ± 17 stomata mm-2). The average number of trichomes per square milimeter was 8 for both the upper and the lower epidermis. The average trichome length was 410 ± 200 mum for the upper epidermis and 460 ± 190 mum for the lower epidermis. Cuticle thickness was not considerably different between lower and upper epidermis. The leaflet blade consisted basically of two layers (upper and lower) of unicellular epidermis, two layers of palisade parenchyma and one layer of spongy parenchyma. One layer of paraveinal mesophyll was found between palisade and spongy parenchyma. In conclusion, leaflets of kudzu present anatomical characteristics that might contribute to the broad physiological plasticity shown by kudzu.Kudzu é uma cultura de cobertura que se tornou invasiva em algumas regiões subtropicais e temperadas. Kudzu tem demonstrado ampla plasticidade fisiológica quando coloniza novos ambientes. Este trabalho teve por objetivo investigar características da anatomia foliar de kudzu que poderiam contribuir para seu hábito invasivo e também para sua propagação em ambientes distintos do ponto de vista climático. Foram analisados cortes frescos e permanentes de lâminas foliares de plantas crescidas no campo. A epiderme inferior tipicamente apresentou maior freqüência de estômatos (147 ± 19 estômatos mm-2) do que a epiderme superior (26 ± 17 estômatos mm-2). O número médio de tricomas por milímetro quadrado foi 8 para ambas, epiderme superior e epiderme inferior. O comprimento médio dos tricomas foi 410 ± 200 mim para a epiderme superior e 460 ± 190 mim para a epiderme inferior. A espessura da cutícula não diferiu significativamente entre as epidermes inferior e superior. A lâmina foliar consistiu basicamente de duas camadas de parênquima paliçádico e uma camada de parênquima lacunoso. Uma camada de mesófilo paranerval foi encontrada entre os parênquimas paliçádico e lacunoso. Concluindo, folíolos de kudzu apresentam características anatômicas que podem contribuir para a ampla plasticidade fisiológica demonstrada pela espécie.1361136