BITS Faculty Publications

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Subject: search.filters.subject.Biological neural networks

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    A novel hardware efficient Digital Neural Network architecture implemented in 130nm technology
    (IEEE, 2010) Gupta, Anu
    Digital Neural Network implementations based on the perceptron model require the use of multi-bit representation of signals and weights. This results in the usage of multi-bit multipliers in each neuron, leading to prohibitively large chip areas. Another problem with hardware implementations of neural networks is the low utilization of chip area due to complex interconnection requirements between successive neuron layers. In this paper we propose an architecture having a single layer of digital neurons that is reused multiple number of times with different weight vectors in order to achieve significant reduction in the required silicon area. The proposed architecture results in a significantly reduced power consumption (55% reduction for an 8 layer, 4 neuron per layer network). The paper also includes the results obtained on implementing the proposed architecture in 130 nm technology using MAGMA blast-fusion design tool.
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    Automatic substructuring for domain decomposition using neural networks
    (IEEE, 2002-08) Ghosal, Sugata
    Application of neural networks for guiding solutions of large numerical problems is an emerging area of research. Automatic generation of subdomains from large 3D finite element meshes is a key preprocessing step in domain decomposition techniques and extremely important for proper load balancing, reducing communication bandwidth and latency, and efficient processor coordination and synchronization in a parallel computing environment. It is desired that the subdomains are approximately of same size, and the total number of interface nodes between adjacent subdomains is minimal. We propose two neural network algorithms employing the philosophy of competitive learning and Hopfield network, that can automatically generate substructures from large 3D meshes with reasonable speed. Both these techniques are implemented in such as a way that they have almost linear complexity w.r.t. the problem size for serial execution. Experimental results show more than 25% improvement over an existing greedy algorithm
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    Neural Network Soft Sensor Application in Cement Industry: Prediction of Clinker Quality Parameters
    (IEEE, 2011) Pani, Ajaya Kumar; Mohanta, Hare Krishna
    A soft sensor tries to estimate difficult to measure quality parameters from the knowledge of easy to measure online process variables. Empirical approach of soft sensor development has gained much popularity recently due to availability of huge quantity of actual process data stored in the industrial database. In this work a soft sensor based on back propagation neural network has been developed for rotary cement kiln. For this purpose, data for all variables associated with rotary cement kiln were collected over a period of one month from a cement industry having a capacity of 10000 tons of clinker production per day. Data preprocessing of the raw data has been performed to remove the anomalies present in the original data. The processed data was used to develop the neural network model of the kiln. Model simulation produced quite satisfactory prediction of free lime, C 3 S, C 2 S and C 3 A.