Towards completely automatic decoder synthesis

Upon receiving the output sequence streaming from a sequen-tial encoder, a decoder reconstructs the corresponding input sequence that streamed to the encoder. Such an encoding and decoding scheme is commonly encountered in commu-nication, cryptography, signal processing, and other applica-tions. Given an encoder specification, decoder design can be error-prone and time consuming. Its automation may help designers improve productivity and justify encoder correct-ness. Though recent advances showed promising progress, there is still no complete method that decides whether a de-coder exists for a finite state transition system. The quest for completely automatic decoder synthesis remains. This paper presents a complete and practical approach to au-tomating decoder synthesis via incremental SAT solving and Craig interpolation. Experiments show that, for decoder-existent cases, our method synthesizes decoders effectively; for decoder-nonexistent cases, our method concludes the non-existence instantly while prior methods may fail

Retrosynthetic reaction prediction using neural sequence-to-sequence models

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step towards solving the challenging problem of computational retrosynthetic analysis

EXTEND-L : an input language for extensible register transfer compilation

This report discusses the model and input language for EXTEND, a synthesis system that permits extensible register transfer synthesis. EXTEND-L fills the need for a language that bridges the gap between existing behavioral input descriptions, which are too abstract, and structural schematics, which cannot capture the high-level behavior. The report first discusses previous work in behavioral synthesis and summarizes the deficiencies of these behavioral specifications. The report then describes the proposed langauge in detail, and concludes with a few examples that show its utility