Abstract: A system includes one or more processors and a statically reconfigurable dataflow processor (SRDAP) coupled to the processors which are programmed to receive a first request to generate an instantiation of a computation graph to generate a probability distribution for N classes and retrieve a compiled graph of the computation graph. The computation graph includes a bias node and a probability distribution node for M classes. The bias node provides a biased tensor of size M to the probability distribution node by adding a bias tensor. The processors generate a bias tensor having N entries equal to zero and M?N entries having negative values and then load the compiled graph with the first bias tensor into a first set coarse-grained reconfigurable units of the SRDAP. Execution of the computation graph is initiated on the SRDAP to generate the probability distribution and a first inference is provided.

Abstract: A source code generation system uses a neural transformer model with attention to predict candidate method bodies given a method docstring, method signature, and one or more method templates. The method templates are derived from intent-snippet pairs from StackOverflow question/answer pairs or template methods from GitHub. Joint embeddings are generated for the method bodies of the method templates and associated method docstrings for quick retrieval. A code completion system uses the source code generation system to generate candidate method bodies to complete a method signature and/or method docstring using the method templates.