Framework for Automatic Translation of Hardware Specifications Written in English to a Formal Language

The most time-consuming component of designing and launching hardware products to market is the verification of Integrated Circuits (IC). An effective way of verifying a design can be achieved by adding assertions to the design. Automatic translation of hardware specifications from natural language to assertions in a formal representation has the potential to improve the verification productivity of ICs. However, natural language specifications have the characteristics of being imprecise, incomplete, and ambiguous. An automation framework can benefit verification engineers only if it is designed with the right balance between the ease of expression and precision of meaning allowed for in the input natural language specifications. This requirement introduces two major challenges for designing an effective translation framework. The first challenge is to allow the processing of expressive specifications with flexible word order variations and sentence structures. The second challenge is to assist users in writing unambiguous and complete specifications in the English language that can be accurately translated.

In this dissertation, we address the first challenge by modeling semantic parsing of the input sentence as a game of BINGO that can capture the combinatorial nature of natural language semantics. BINGO parsing considers the context of each word in the input sentence to ensure high precision in the creation of semantic frames.

We address the second challenge by designing a suggestion and feedback framework to assist users in writing clear and coherent specifications. Our feedback generates different ways of writing acceptable sentences when the input sentence is not understood.

We evaluated our BINGO model on 316 hardware design specifications taken from the documents of AMBA, memory controller, and UART architectures. The results showed that highly expressive specifications could be handled in our BINGO model. It also demonstrated the ease of creating rules to generate the same semantic frame for specifications with the same meaning but different word order.

We evaluated the suggestion and rewriting framework on 132 erroneous specifications taken from AMBA and memory controller architectures documents. Our system generated suggestions for all the specs. On manual inspection, we found that 87% of these suggestions were semantically closer to the intent of the input specification. Moreover, automatic contextual analysis of the rewritten form of the input specification allowed the translation of the input specification with different words and different order of words that were not defined in our grammar.