Impact of Structural Faults on Neural Network Performance

Deep Learning (DL), a subset of Artificial Intelligence (AI), is growing rapidly with possible applications in different domains such as speech recognition, computer vision etc. Deep Neural Network (DNN), the backbone of DL algorithms is a directed graph containing multiple layers with different number of neurons residing in each layer. The use of these networks has been increased in the last few years due to availability of large data sets and huge computation power. As the size of DNN is growing over the years, researchers have developed specialized hardware accelerators to reduce the inference compute time. An example of such domain specific architecture designed for Neural Network acceleration is Tensor Processing Unit (TPU) which outperforms GPU in the inference stage of DNN execution. The heart of this inference engine is a Matrix Multiplication unit which is based on systolic array architecture. The TPU\u27s systolic array is a grid-like structure made of individual processing elements that can be extended along rows and columns. Due to external environmental factors or internal scaling of semiconductor, these systems are often prone to faults which leads to improper calculations and thereby resulting in inaccurate decisions by the DNN. Although a lot of work has been done in the past on the computing array implementation and it\u27s reliability concerns, their fault tolerance behavior for DNN application is not very well understood. It is not even clear what would be the impact of various different faults on the accuracy. We in this work, first study possible mapping strategies to implement a convolution and dense layer weights on TPU systolic array. Next we consider various faults scenarios that may occur in the array. We divide these fault scenarios into low, high row and column faults (Fig. 1(a) pictorially represents column faults) modes with respect to the multiplication unit. Next, we study the impact of these fault models on the overall accuracy of the DNN performance on a faculty TPU unit. The goal is to study the resiliency and overcome the limitations of earlier work. The previous work was very effective in masking the random faults which used pruning of weights (removing weights or connections in the DNN) plus retraining to mask the faults on the array. However, it failed in the case of column faults which is clearly shown in Fig. 1(b). We also propose techniques to mitigate or bypass the row and column faults. Our mapping strategy follows physical_x(i) = i%N and physical_y(j) = j%N where (i,j) represents the index of dense (FC) weight matrix and (physical x(i), physical y(j)) indicates the actual physical location on the array of size N. The convolution filters are linearized with respect to every channel so as to convert them into proper weight matrix and mapped according to the previous mentioned policy. It was shown that DNNs can up to certain faults in the array while retaining the original accuracy (low row faults). The accuracy of the network decreases even with one column faults if it (column) is in the use. As per the results, it is proved that for the same number of row and column faults, the latter has most impact on the network accuracy because pruning input neuron has very little effect than pruning an output neuron. We experimented with three different networks and found the influence of these different faults to be the same. These faults can be mitigated using techniques like Matrix Transpose and Array Reduction which does not require retraining of weights. For low row faults, the original mapping policy can be retained such that weights can be mapped at their exact locations which does not affect the accuracy. Low column faults can be converted into low row faults by transposing the matrix. In the case of high row (column) faults, the entire row (column) has to be avoided to completely bypass the faulty locations. Static mapping of weights along with retraining the network on the array can be effective in the case of random faults. Adapting to change in the case of structured faults can reduce the burden of retraining which happens outside the TPU

Temperature-dependent device properties of gamma-CuI and beta-Ga2O3 heterojunctions

Temperature-dependent studies of Ga2O3-based heterojunction devices are important in understanding its carrier transport mechanism, junction barrier potential, and stability at higher temperatures. In this study, we investigated the temperature-dependent device characteristics of the p-type gamma-copper iodide (gamma-CuI)/n-type beta-gallium oxide (beta-Ga2O3) heterojunctions, thereby revealing their interface properties. The fabricated gamma-CuI/beta-Ga2O3 heterojunction showed excellent diode characteristics with a high rectification ratio and low reverse saturation current at 298 K in the presence of a large barrier height (0.632 eV). The temperature-dependent device characteristics were studied in the temperature range 273-473 K to investigate the heterojunction interface. With an increase in temperature, a gradual decrease in the ideality factor and an increase in the barrier height were observed, indicating barrier inhomogeneity at the heterojunction interface. Furthermore, the current-voltage measurement showed electrical hysteresis for the reverse saturation current, although it was not observed for the forward bias current. The presence of electrical hysteresis for the reverse saturation current and of the barrier inhomogeneity in the temperature-dependent characteristics indicates the presence of some level of interface states for the gamma-CuI/beta-Ga2O3 heterojunction device. Thus, our study showed that the electrical hysteresis can be correlated with temperature-dependent electrical characteristics of the beta-Ga2O3-based heterojunction device, which signifies the presence of surface defects and interface states

Artificial Intelligence in Various Sectors

The junction of AI and computer security is an area of increasing concern, due to the imminent application of AI to fielded systems. Two new areas of research need are identified: artificial intelligence techniques in the development of secure systems. An artificial intelligence system developed for a commercial bank to increase the productivity and effectiveness of funds transfer telex request operations. These telexes were previously processed manually. The advancement in computer technology has encouraged the researchers to develop software for assisting doctors in making decision without consulting the specialists directly

Impact of land use changes and management practices on groundwater resources in Kolar district, Southern India

Study region: This study analyzes the impact of land use changes on the hydrology of Kolar district in the state of Karnataka, India. Kolar receives on average 565 mm (σ = 130) rainfall during June to October and has a wide gap between its water supply and demand. Study focus: This research identifies the reasons and causes of the gap. A water balance model was successfully calibrated and validated against measurements of groundwater level, recharge and surface runoff. New hydrological insights for the region: The study revealed that between 1972 and 2011, there was a major shift from grass and rainfed crop lands to eucalyptus plantation and irrigated cultivation. About 17.7 % and 18 % of the district area converted into eucalyptus plantation and irrigated lands during this period, respectively. Eucalyptus plantations tended to cause large losses by ET leading to increase in soil moisture deficit and reduction in the recharge to groundwater and in surface runoff (approx. 30 %). The irrigation demand of the district increased from 57 mm (1972) to 140 mm (2011) which resulted in increased groundwater abstraction by 145 %. The expansion of the irrigated area is the major contributing factor for widening the demand-supply gap (62 %) of the freshwater availability. Results could help various stakeholders, including district and national authorities to develop the most suitable water management strategies in order to close the gap between water supply and demand

Characteristics of Vertical Ga2O3 Schottky Junctions with the Interfacial Hexagonal Boron Nitride Film

We present the device properties of a nickel (Ni)- gallium oxide (Ga2O3) Schottky junction with an interfacial hexagonal boron nitride (hBN) layer. A vertical Schottky junction with the configuration Ni/hBN/Ga2O3/In was created using a chemical vapor-deposited hBN film on a Ga(2)O(3 )substrate. The current-voltage characteristics of the Schottky junction were investigated with and without the hBN interfacial layer. We observed that the turn-on voltage for the forward current of the Schottky junction was significantly enhanced with the hBN interfacial film. Furthermore, the Schottky junction was analyzed under the illumination of deep ultraviolet light (254 nm), obtaining a photoresponsivity of 95.11 mA/W under an applied bias voltage (-7.2 V). The hBN interfacial layer for the Ga2O3-based Schottky junction can serve as a barrier layer to control the turn-on voltage and optimize the device properties for deep-UV photosensor applications. Furthermore, the demonstrated vertical heterojunction with an hBN layer has the potential to be significant for temperature management at the junction interface to develop reliable Ga2O3-based Schottky junction devices

(Z)-2-(4-Nitro­benzyl­idene)-1-benzofuran-3(2H)-one

In the crystal structure of the title compound, C15H9NO4, weak C—H⋯O inter­actions generate rings with R 2 2(8) motifs. The supra­molecular aggregation is completed by the presence of C—H⋯O and van der Waals inter­actions

Harnessing Sorghum Landraces to Breed High-Yielding, Grain Mold-Tolerant Cultivars With High Protein for Drought-Prone Environments

Intermittent drought and an incidence of grainmold disease are the twomajor constraints affecting sorghum production and productivity. The study aimed at developing drought-tolerant sorghum varieties possessing a high protein content and tolerance to grain mold with stable performance using additive main effects and multiplicative interaction (AMMI) and genotype and genotype × environment interaction (GGE) biplot methods. Systematic hybridization among the 11 superior landraces resulted in subsequent pedigree-based breeding and selection from 2010 to 2015 evolved 19 promising varieties of grains such as white, yellow, and brown pericarp grains. These grain varieties were evaluated for their adaptability and stability for yield in 13 rainfed environments and for possessing tolerance to grain mold in three hot spot environments. A variety of yellow pericarp sorghum PYPS 2 (3,698 kg/ha; 14.52% protein; 10.70 mg/100 g Fe) possessing tolerance to grain mold was identified as a stable variety by using both AMMI and GGE analyses. Four mega-environments were identified for grain yield and fodder yield. Sorghum varieties PYPS 2, PYPS 4, PYPS 8, and PYPS 11 were highly stable in E2 with a low grain mold incidence. Besides meeting the nutritional demand of smallholder farmers under dryland conditions, these varieties are suitable for enhancing sorghum productivity under the present climate change scenario