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: The amount of speed-up that can be obtained by optimizing the program to run on a different architecture is determined by static measurements of the program. Multiple such static measurements are processed by a machine learning system after being discretized to alter their accuracy vs precision. Static analysis requires less analysis overhead and permits analysis of program portions to optimize allocation of porting resources on a large program.

Abstract: The amount of speed-up that can be obtained by optimizing the program to run on a different architecture is determined by static measurements of the program. Multiple such static measurements are processed by a machine learning system after being discretized to alter their accuracy vs precision. Static analysis requires less analysis overhead and permits analysis of program portions to optimize allocation of porting resources on a large program.