Data processing system configured for separated computations for positive and negative data

A digital accelerator comprising:

• a processor configured to obtain input data, the processor comprising:
• a first arithmetic logic unit configured to perform computations on the input data to determine one or more first results;
• a second arithmetic logic unit configured to perform computations on the input data to determine one or more second results, wherein the first arithmetic logic unit and the second arithmetic logic unit operate in parallel and are configured to separate the one or more first results and the one or more second results into a first subset of data and a second subset of data, the first subset of data including positive data and the second subset of data including negative data;
• a positive adder configured to aggregate the first subset of data;
• a negative adder configured to aggregate the second subset of data;
• and
• a memory configured to store the first subset of data and the second subset of data;
• the processor configured to:
• determine a solution based on aggregating the first subset of data and the second subset of data;
• and
• execute an application using a machine learning model or a deep neural network based on the solution.

One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause a system to perform operations, the operations comprising:

• obtaining input data;
• performing computations on the input data using a first arithmetic logic unit to determine one or more first results;
• performing computations on the input data using a second arithmetic logic unit to determine one or more second results, wherein the first arithmetic logic unit and the second arithmetic logic unit operate in parallel;
• separatingthe one or more first results and the one or more second results into a first subset of data and a second subset of data, the first subset of data including positive data and the second subset of data including negative data;
• aggregating, using a positive adder, the first subset of data;
• aggregating, using a negative adder, the second subset of data;
• determining a solution based on the aggregating the first subset of data and the second subset of data;
• and
• executing an application using a machine learning model or a deep neural network based on the solution.