# US 2025/0147760 A1 · Processor and control method for processor > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0147760 A1 - Document ID: US20250147760A1 - Status: Published application - Kind: A1 - Application number: 19/013,344 - Filed: 2025-01-08 - Published: 2025-05-08 - Earliest claimed: 2018-11-09 - Assignees: Preferred Networks, Inc. - Applicants: Preferred Networks, Inc. - Inventors: Tanvir AHMED - Web page: https://patentgrep.com/patent/US20250147760A1 ## CPC - G06F 9/3001: {Arithmetic instructions} - G06N 3/063: using electronic means - G06F 9/30145: {Instruction analysis, e.g. decoding, instruction word fields} - G06F 15/8046: {Systolic arrays} - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A processor includes a plurality of processing elements, wherein the processor is configured to execute, by using one or more first processing elements among the plurality of processing elements, operations of a first layer of a neural network including a plurality of layers, the processor is configured to execute, by using one or more second processing elements among the plurality of processing elements, operations of a second layer of the neural network, the one or more first processing elements and the one or more second processing elements do not entirely overlap, and the second layer is at a later processing stage than the first layer. ## Claims ### Claim 1 A processor comprising: a plurality of processing elements, wherein the processor is configured to execute, by using one or more first processing elements among the plurality of processing elements, operations of a first layer of a neural network including a plurality of layers, the processor is configured to execute, by using one or more second processing elements among the plurality of processing elements, operations of a second layer of the neural network, the one or more first processing elements and the one or more second processing elements do not entirely overlap, and the second layer is at a later processing stage than the first layer. ### Claim 2 (depends on 1) The processor as claimed in claim 1, further comprising a plurality of memories, wherein the processor is configured to use one or more first memories among the plurality of memories for the execution of the operations of the first layer, the processor is configured to use one or more second memories among the plurality of memories for the execution of the operations of the second layer, and the one or more first memories and the one or more second memories do not entirely overlap. ### Claim 3 (depends on 2) The processor as claimed in claim 2, wherein the one or more first memories and the one or more second memories each store at least one of an operand or an instruction. ### Claim 4 (depends on 2) The processor as claimed in claim 2, wherein the processor is configured to transmit, during the operations of the first layer, at least one of a weight, an operand or an instruction to the one or more second memories. ### Claim 5 (depends on 1) The processor as claimed in claim 1, wherein the one or more first processing elements includes at least one processing element that is not included in the one or more second processing elements, and the one or more second processing elements includes at least one processing element that is not included in the one or more first processing elements. ### Claim 6 (depends on 1) The processor as claimed in claim 1, wherein the plurality of processing elements include at least one processing element that is included in neither the one or more first processing elements nor the one or more second processing elements. ### Claim 7 (depends on 6) The processor as claimed in claim 6, wherein the at least one processing element that is included in neither the one or more first processing elements nor the one or more second processing elements is used for executing operations of a third layer of the neural network. ### Claim 8 (depends on 1) The processor as claimed in claim 1, wherein the processor is configured to execute, by using one or more third processing elements among the plurality of processing elements, operations of a third layer of the neural network, the one or more first processing elements, the one or more second processing elements, and the one or more third processing elements do not entirely overlap each other, and the third layer being at a later processing stage than the second layer. ### Claim 9 (depends on 1) The processor as claimed in claim 1, wherein the one or more first processing elements are wholly different from the one or more second processing elements. ### Claim 10 (depends on 1) The processor as claimed in claim 1, wherein the processor is configured to reconfigure the plurality of processing elements for operations of each layer of the neural network. ### Claim 11 (depends on 1) The processor as claimed in claim 1, further comprising a plurality of information transfer circuits, wherein the processor is configured to use one or more first information circuits among the plurality of information transfer circuits for the execution of the operations of the first layer, the processor is configured to use one or more second information transfer circuits among the plurality of information transfer circuits for the execution of the operations of the second layer, the one or more first information transfer circuits and the one or more second information transfer circuits do not entirely overlap. ### Claim 12 (depends on 11) The processor as claimed in claim 11, wherein each of the plurality of processing elements is connected to at least one output memory, wherein the at least one output memory stores at least one operation result of at least one processing element among the plurality of processing elements, and wherein the at least one operation result stored in the at least one output memory is transmitted to at least one information transfer circuits among the plurality of information transfer circuits. ### Claim 13 (depends on 1) The processor as claimed in claim 1, wherein the plurality of processing elements are arranged in a matrix of two or more rows and two or more columns. ### Claim 14 (depends on 10) The processor as claimed in claim 10, wherein the plurality of processing elements are reconfigured to include neighboring processing elements for the operations of each layer of the neural network. ### Claim 15 (depends on 1) The processor as claimed in claim 1, wherein the plurality of layers of the neural network include at least one of a convolution layer, a pooling layer, an activating layer, or a fully-connected layer. ### Claim 16 (depends on 1) The processor as claimed in claim 1, further comprising a plurality of memories, wherein the processor is configured to use a first resource for the execution of the operations of the first layer, the first resource including one or more first memories among the plurality of memories and the one or more first processing elements, wherein the processor is configured to use a second resource for the execution of the operations of the second layer, the second resource including one or more second memories among the plurality of memories and the one or more second processing elements, and wherein the one or more first memories and the one or more second memories do not entirely overlap. ### Claim 17 (depends on 16) The processor as claimed in claim 16, wherein the first resource and the second resource are constructed based on operations for the neural network, and a size of the first resource is different from a size of the second resource. ### Claim 18 (depends on 16) The processor as claimed in claim 16, wherein the processor is configured to transmit, during the execution of the operations of the second layer, operation results of the first layer to a third resource used in operations of a third layer that is at a later processing stage than the second layer in the neural network, the third resource including one or more third memories among the plurality of memories and one or more third processing elements among the plurality of processing elements. ### Claim 19 (depends on 16) The processor as claimed in claim 16, wherein the operations of the first layer and the operations of the second layer include convolutional operations. ### Claim 20 A processing method implemented by a plurality of processing elements, comprising: executing, by using one or more first processing elements among the plurality of processing elements, operations of a first layer of a neural network including a plurality of layers; and executing, by using one or more second processing elements among the plurality of processing elements, operations of a second layer of the neural network, wherein the one or more first processing elements and the one or more second processing elements do not entirely overlap, and wherein the second layer is at a later processing stage than the first layer. ### Claim 21 (depends on 20) The processing method as claimed in claim 20, further comprising: accessing one or more first memories, among a plurality of memories, for the execution of the operations of the first layer; and accessing one or more second memories among the plurality of memories for the execution of the operations of the second layer, wherein the one or more first memories and the one or more second memories do not entirely overlap. ### Claim 22 (depends on 21) The processing method as claimed in claim 21, further comprising transmitting, during the execution of the operations of the first layer, at least one of a weight, an operand or an instruction to the one or more second memories. ### Claim 23 (depends on 20) The processing method as claimed in claim 20, further comprising executing, by using one or more third processing elements among the plurality of processing elements, operations of a third layer of the neural network, the one or more first processing elements, the one or more second processing elements, and the one or more third processing elements do not entirely overlap each other, and the third layer being at a later processing stage than the second layer. ### Claim 24 (depends on 20) The processing method as claimed in claim 20, further comprising reconfiguring the plurality of processing elements for operations of each layer of the neural network. ### Claim 25 (depends on 20) The processing method as claimed in claim 20, further comprising: using one or more first information circuits, among a plurality of information transfer circuits, for the execution of the operations of the first layer, using one or more second information transfer circuits among the plurality of information transfer circuits for the execution of the operations of the second layer, wherein the one or more first information transfer circuits and the one or more second information transfer circuits do not entirely overlap. ### Claim 26 (depends on 25) The processing method as claimed in claim 25, further comprising: causing at least one output memory, connected to each of the plurality of processing elements, to store at least one operation result of at least one processing element among the plurality of processing elements, and transmitting the at least one operation result stored in the at least one output memory to at least one information transfer circuits among the plurality of information transfer circuits. ### Claim 27 (depends on 20) The processing method as claimed in claim 20, further comprising reconfiguring the plurality of processing elements to include neighboring processing elements for the operations of each layer of the neural network. ### Claim 28 (depends on 20) The processing method as claimed in claim 20, further comprising: using a first resource for the execution of the operations of the first layer, the first resource including one or more first memories among the plurality of memories and the one or more first processing elements; and using a second resource for the execution of the operations of the second layer, the second resource including one or more second memories among the plurality of memories and the one or more second processing elements, and wherein the one or more first memories and the one or more second memories do not entirely overlap. ### Claim 29 (depends on 28) The processing method as claimed in claim 28, further comprising transmitting, during the execution of the operations of the second layer, operation results of the first layer to a third resource used in operations of a third layer that is at a later processing stage than the second layer in the neural network, the third resource including one or more third memories among the plurality of memories and one or more third processing elements among the plurality of processing elements.