Abstract: Techniques including receiving a first set of values for processing by a machine learning (ML) network, storing a first portion of the first set of values in an on-chip memory, processing the first portion of the first set of values in a first layer of the ML network to generate a second portion of a second set of values, overwriting the stored first portion with the generated second portion, processing the second portion in a second layer of the ML network to generate a third portion of a third set of values, storing the third portion, repeating the steps of storing the first portion, processing the first portion, overwriting the stored first portion, processing the second portion, and storing the third portion for a fourth portion of the first set of values until all portions of the first set of values are processed to generate the third set of values.

Abstract: An embedded multiprocessor system is provided that includes a multiprocessor system on a chip (SOC), a memory coupled to the multiprocessor SOC, the memory storing application software partitioned into an initial boot stage and at least one additional boot stage, and a secondary boot loader configured to boot load the initial boot stage on at least one processor of the multiprocessor SOC, wherein the initial boot stage begins executing and flow of data from the initial boot stage to the at least one additional boot stage is disabled, wherein the application software is configured to boot load a second boot stage of the at least one additional boot stage on at least one other processor of the multiprocessor SOC and to enable flow of data between the initial boot stage and the second boot stage.

Abstract: An electronic device, comprising one or more processors, wherein the one or more processors are configured to execute instructions causing the one or more processors to: receive a machine learning (ML) model and execution information associated with the ML model, wherein the execution information including first execution data indicating how to execute the ML model optimized based on a first performance criterion, and second execution data execution data indicating how to execute the ML model optimized based on a second performance criteria, the second performance criterion different from the first performance criteria; execute the ML model based on the first execution data; determine to execute the ML model based on the second execution data; and execute the ML model based on the second execution data.

Abstract: Techniques for enhancing machine learning (ML) model execution. The technique includes determining an amount of memory used to process layers of a machine learning network having multiple layers, smoothing the amount of memory used to process the layers of the machine learning network based on a number of layers, identifying change layers where the smoothed amount of memory used changes more than a memory change threshold amount, grouping the layers of the machine learning network into a first layer grouping based on the identified change layers, and outputting the first layer grouping.

Abstract: An embedded multiprocessor system is provided that includes a multiprocessor system on a chip (SOC), a memory coupled to the multiprocessor SOC, the memory storing application software partitioned into an initial boot stage and at least one additional boot stage, and a secondary boot loader configured to boot load the initial boot stage on at least one processor of the multiprocessor SOC, wherein the initial boot stage begins executing and flow of data from the initial boot stage to the at least one additional boot stage is disabled, wherein the application software is configured to boot load a second boot stage of the at least one additional boot stage on at least one other processor of the multiprocessor SOC and to enable flow of data between the initial boot stage and the second boot stage.

Abstract: An embedded multiprocessor system is provided that includes a multiprocessor system on a chip (SOC), a memory coupled to the multiprocessor SOC, the memory storing application software partitioned into an initial boot stage and at least one additional boot stage, and a secondary boot loader configured to boot load the initial boot stage on at least one processor of the multiprocessor SOC, wherein the initial boot stage begins executing and flow of data from the initial boot stage to the at least one additional boot stage is disabled, wherein the application software is configured to boot load a second boot stage of the at least one additional boot stage on at least one other processor of the multiprocessor SOC and to enable flow of data between the initial boot stage and the second boot stage.

Abstract: Techniques including receiving a first set of values for processing by a machine learning (ML) network, storing a first portion of the first set of values in an on-chip memory, processing the first portion of the first set of values in a first layer of the ML network to generate a second portion of a second set of values, overwriting the stored first portion with the generated second portion, processing the second portion in a second layer of the ML network to generate a third portion of a third set of values, storing the third portion, repeating the steps of storing the first portion, processing the first portion, overwriting the stored first portion, processing the second portion, and storing the third portion for a fourth portion of the first set of values until all portions of the first set of values are processed to generate the third set of values.

Abstract: An embedded multiprocessor system is provided that includes a multiprocessor system on a chip (SOC), a memory coupled to the multiprocessor SOC, the memory storing application software partitioned into an initial boot stage and at least one additional boot stage, and a secondary boot loader configured to boot load the initial boot stage on at least one processor of the multiprocessor SOC, wherein the initial boot stage begins executing and flow of data from the initial boot stage to the at least one additional boot stage is disabled, wherein the application software is configured to boot load a second boot stage of the at least one additional boot stage on at least one other processor of the multiprocessor SOC and to enable flow of data between the initial boot stage and the second boot stage.