{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11476869","patent":{"patent_number":"US-11476869","title":"Dynamically partitioning workload in a deep neural network module to reduce power consumption","assignee":null,"inventors":[],"filing_date":"2018-04-13T00:00:00.000Z","publication_date":"2022-10-18T00:00:00.000Z","cpc_codes":["G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06F","G06N","G06N","G06N","G06N","G06N","G06N","G06N","G06N","H04L","H04L","H04L","G06F","G06F","G06F","G06F","G06F","G06F","H04L"],"num_claims":18,"abstract":"A deep neural network (DNN) module is disclosed that can dynamically partition neuron workload to reduce power consumption. The DNN module includes neurons and a group partitioner and scheduler unit. The group partitioner and scheduler unit divides a workload for the neurons into partitions in order to maximize the number of neurons that can simultaneously process the workload. The group partitioner and scheduler unit then assigns a group of neurons to each of the partitions. The groups of neurons in the DNN module process the workload in their assigned partition to generate a partial output value. The neurons in each group can then sum their partial output values to generate a final output value for the workload. The neurons can be powered down once the groups of neurons have completed processing their assigned workload to reduce power consumption."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Dynamically partitioning workload in a deep neural network module to reduce power consumption","description":"A deep neural network (DNN) module is disclosed that can dynamically partition neuron workload to reduce power consumption. The DNN module includes neurons and a group partitioner and scheduler unit. ","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11476869","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-11476869","citation_suggestion":"Patentable. \"Dynamically partitioning workload in a deep neural network module to reduce power consumption\" (US-11476869). https://patentable.app/patents/US-11476869","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11476869","json":"https://patentable.app/api/llm-context/US-11476869","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T08:37:18.617Z"}