Method, product, and apparatus for a multidimensional processing array for hardware acceleration of convolutional neural network inference

A method, comprising:

• receiving a machine learning processing job;
• executing the machine learning processing job using parallel processing of multiple output pixels during a cycle at least by walking data across processing elements and broadcasting a single weight in a corresponding shared weight memory to a plurality of processing elements within a corresponding region for use in performing parallel multiplication operations, wherein each region of a plurality of regions includes a plurality of processing elements and a respective shared weight memory of a plurality of shared weight memories that is coupled to the plurality of processing elements in the corresponding region;
• and
• generating an output indicating whether the machine learning processing job was successful or failed.

A non-transitory computer readable medium, having stored thereon a set of configuration information for configuring a gate array or generating an application specific integrated circuit, the set of configuration information, when implemented performs a set of acts, the set of acts comprising:

• receiving a machine learning processing job;
• executing parallel processing of the machine learning processing job, wherein the parallel processing operates on multiple output pixels using a shared weight during a corresponding cycle at least by broadcasting a single weight in a corresponding shared weight memory to a plurality of processing elements within a corresponding region and data is walked across processing elements after the corresponding cycle, wherein each region of a plurality of regions includes a plurality of processing elements and a respective shared weight memory of a plurality of shared weight memories that is coupled to the plurality of processing elements in the corresponding region;
• and
• generating an output indicating whether the machine learning processing job was successful or failed.