S. Sankar Ganesh

The neural network computations for Artificial Intelligence (AI) applications demand high speed, low power and area-efficient Floating-Point (FP) multiplication. In this work, we propose an efficient unsigned Bit Pair Recoding (BPR) algorithm for area, power, and speed improved FP unsigned mantissa multiplication. The partial product rows are reduced from n to $\frac {n}{4}$ for $n\times n$ binary multiplier using the BPR algorithm with parallel processed partial product reduction. The new algorithm performs partial product row reduction without the 2’s complement, Negative Encoding (NE), and Sign Extension (SE) are required for Booth recoded-based multiplication but these computations are not required for floating point unsigned multiplication. The computational cost of determining a 2’s complementary circuit and neglecting the sign bit extension of each partial product row in the Modified Booth Encoding (MBE) algorithm is effectively eliminated by BPR algorithm. The unsigned mantissa multiplication using partial product array reduction with the BPR technique uses 27.5% less area, 18% less power, and 33.33% improved speed for generating one partial product row than conventional Booth multipliers. The $8\times 8$ and $16\times 16$ multipliers are used to verify the BPR binary multipliers on TSMC 65nm 1.1 V CMOS standard cell library and the synthesis reports are compared with the conventional and best-reported improved Booth multipliers. Finally, the MAC design uses 16-bit FP arithmetic with an $8\times 8$ mantissa multiplier for the CNN accelerator is developed, and it is validated with suitable error metrics like Mean Relative Error (MRE) to assess the suggested architecture for AI applications.

Air is the most vital constituent for the sustenance of life on earth. Air pollution is the major problem we have been facing. It is important to address this issue to lead a healthy life. Forecasting of air quality will contribute to a healthy society. In this paper, artificial neural network (ANN) predictors trained with conjugate gradient descent have been implemented to forecast air quality index (AQI) in a particular area of interest. Several neural network models such as multilayer perceptron (MLP), Elman, radial basis function and NARX were applied. In these neural network models, four major pollutant concentrations including NO

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Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index (AQI), artificial neural networks (ANNs) trained with conjugate gradient descent (CGD), such as multilayer perceptron (MLP), cascade forward neural network, Elman neural network, radial basis function (RBF) neural network, and nonlinear autoregressive model with exogenous input (NARX) along with regression models such as multiple linear regression (MLR) consisting of batch gradient descent (BGD), stochastic gradient descent (SGD), mini-BGD (MBGD) and CGD algorithms, and support vector regression (SVR), are implemented. In these models, the AQI is the dependent variable and the concentrations of NO ₂ , CO, O ₃ , PM _2.5 , SO ₂ , and PM ₁₀ for the years 2010–2016 in Houston and Los Angeles are the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors are presented. This proposed approach performs with the highest efficiency in terms of forecasting air quality index.

It is always important to monitor the quality of air that we inhale to protect ourselves from the respiratory diseases. In this paper, we present different regression models to forecast air quality index (AQI) in particular areas of interest. Support vector regression (SVR) and linear models like multiple linear regression consisting of gradient descent, stochastic gradient descent, mini-batch gradient descent were implemented. In these models, the air quality index (AQI) is dependent on pollutant concentrations of NO₂, CO, O₃, PM_2.5, PM₁₀ and SO₂. Among these models, support vector regression (SVR) exhibited high performance in terms of investigated measures of quality.

This paper aims to establish a non-linear regression model based on Mamdani fuzzy inference system and examine the performance of the model based on various metrics such as correlation, index of agreement, mean absolute percentage error, mean absolute error and root mean square error. The data used in this operation is collected from Delhi Punjabibagh with pollutants spanning from Nitrogen dioxide to particulate matter 10μm with ozone and carbon monoxide considered to comprehensively cover the base of inputs. The resultant air quality index is then measured with the calculated ones based on the metrics discussed above.