Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that i) approach state-of-the-art classification accuracy across 8 standard datasets, encompassing vision and speech, ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1200 and 2600 frames per second and using between 25 and 275 mW (effectively > 6000 frames / sec / W) and iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. For the first time, the algorithmic power of deep learning can be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.Comment: 7 pages, 6 figure

International Virtual Conference on Computational Mathematics

ICCM 2021 aims to bring together both novice and experienced scientists with developers, to meet new colleagues, collect new ideas and establish new cooperation between research groups.</jats:p

Validating VCP using model based approach

Verification cases and procedures (VCP) are required to provide the procedure and approach to verify the functional and non-functional requirements of a system on target. Reviews are required to check the correctness and completeness of these VCP's which depends on time and reviewer's experience. This paper proposes a novel approach to validate VCPs that are created using conventional method. Modeling of the functional requirements [1] [2] is carried out using Matlab Simulink R2013a. The approach has been evaluated for correctness by running it on one of the critical functionalities of an indigenously developed safety critical system

Assessment of Significant Risk Factors in Different Construction Stages.

Construction industry plays as a major sector In India. Among the various management system in project, Risk management system plays as a vital role. Risk management involves in various stage (i.e.) Identification, Estimating, planning and development. So in order to know the influential factors that concern in implementation of risk, a study must be made. Thus, pilot study is conducted to find the factors. A face to face interview is conducted to improve the efficiency of questionnaire. The responses are collected from the building professionals (contractors, engineers, architects, etc.) with the questionnaire containing 45 factors and used for analysis. The analysis process is done with the help of SPSS and AMOS software for collected sample. Based on the result a hierarchy of factors is created and prioritization of factors is done. Preventive measures are listed to minimize the degree of risk to a limited value</jats:p

Construction Material Management through Inventory Control Techniques

Objectives: A research has shown that the construction materials account for more than 55%-60% of the total expenditure of a project. Efficient material planning plays a major role in the successful handing over of a project within the estimated cost and schedule. Methods: This paper mainly focuses on materials planning and inventory control as these are the major aspects of material management. S-curve analysis is performed to measure the fluctuation between estimated materials cost and market materials cost. The major reasons for this are identified by interviewing engineers and contractors. Inventory control techniques such as ABC classification as well as EOQ analysis are conducted. Findings: The result of the S-curve analysis show that the actual materials cost is higher than the planned materials cost in most cases. ABC and EOQ analysis are applied to maintain sufficient stock in inventory and any given point of time, to protect the materials in the inventory against damages, to reduce inventory holding costs, to overcome stock-out problems and to maintain the inventory in an optimal level. Sensitivity check is applied to the results of EOQ analysis. The stock-out of A class and B class material problems faced in the  construction site can be reduced by  the application of ABC classification and BOQ analysis. The total expenditure of inventory is less after the adoption of these simple inventory control techniques.  Conclusion: Instead of using costly software for inventory management, the engineers and contractors may use these simple inventory control techniques which are equally beneficial and economical.   </jats:p