This NNEF parser converts neural network information into a parsing tree and quantizes data. The proposed converter includes a lexical, syntax, and parser. The proposed technique standardizes the graphic expression grammar and learning data storage format using the Neural Network Exchange Format (NNEF) of Khronos. This study presents interworking techniques for ensuring the compatibility of neural networks and data among the various deep learning frameworks. There is a growing demand for standardization to solve this problem. However, each inference engine has its own neural network storage format. The results are recorded and analyzed for time taken to convert the NL commands to code and the efficiency of the implementation is measured with accuracy, precision and recall.īased on the growing demand for neural network technologies, various neural network inference engines are being developed. Lex is used for tokenization and the LALR parser of Yacc processes the syntax specifications to generate an output procedure. Using Python libraries NLTK and ply libraries, conversion of human language data to programmable C codes is done and the code is returned to the client. The code editor is a web page and the user instructions are sent to a Flask server for processing. C is used as the programming language in the proposed model. Lexical, syntax and semantic analysis is performed on the user's instructions and then the code is generated. The designed solution is an application based on speech recognition and user interaction to make coding faster and efficient. The aim of the proposed model is to do away with the syntactic structure of a programming language and the user can specify the instructions in human interactive form, using either text or speech. Programmers should know the syntax of the programming language in order to code. There is a gap observed between the natural language (NL) of speech and writing a program to generate code.

Lex compiler editor online