Intel Corporation

Arizona Board of Regents, a Body Corporate

Arvind Sudarsanam

Kent D Glossop

Rakesh Krishnaiyer

Sethuraman Panchanathan

Aravind Dasu

Rajiv Deodhar

Daniel Woodworth

Karthikeyan Vaithianathan

Ali Akoglu

Sergey Dmitriev

Systems, apparatuses and methods may provide for technology that detects one or more local variables in source code, wherein the local variable(s) lack dependencies across iterations of a loop in the source code, automatically generate pipeline execution code for the local variable(s), and incorporate the pipeline execution code into an output of a compiler. In one example, the pipeline execution code includes an initialization of a pool of buffer storage for the local variable(s).

Automatic compiler dataflow optimization to enable pipelining of loops with local storage requirements

Embodiments of a near optimal configurable adder tree for arbitrary shaped 2D block sum of absolute differences (SAD) calculation engine are generally described herein. Other embodiments may be described and claimed. In some embodiments, a configurable two-dimensional adder tree architecture for computing a sum of absolute differences (SAD) for various block sizes up to 16 by 16 comprises a first stage of one-dimensional adder trees and a second stage of one-dimensional adder trees, wherein each one-dimensional adder tree comprises an input routing network, a plurality of adder units, and an output routing network.

Near optimal configurable adder tree for arbitrary shaped 2D block sum of absolute differences (SAD) calculation engine

A method of producing a reconfigurable circuit device for running a computer program of moderate complexity such as multimedia processing. Code for the application is compiled into Control Flow Graphs representing distinct parts of the application to be run. From those Control Flow Graphs are extracted basic blocks. The basic blocks are converted to Data Flow Graphs by a compiler utility. From two or more Data Flow Graphs, a largest common subgraph is determined. The largest common subgraph is ASAP scheduled and substituted back into the Data Flow Graphs which also have been scheduled. The separate Data Flow Graphs containing the scheduled largest common subgraph are converted to data paths that are then combined to form code for operating the application. The largest common subgraph is effected in hardware that is shared among the parts of the application from which the Data Flow Graphs were developed. Scheduling of the overall code is effected for sequencing, providing fastest run times and the code is implemented in hardware by partitioning and placement of processing elements on a chip and design of the connective fabric for the design elements.

Reconfigurable processing

Growing community of inventors

Logan, UT, United States of America

Arvind Sudarsanam