Abstract: A computer-implemented method includes receiving a strategy associated with a new machine-learning (ML) project. There exist a plurality of ML projects, each of which includes artifacts, and for each such candidate project, the following are performed: iterations of the candidate ML project are divided into a first phase, including a first set of iterations, and a second phase, including a second set of iterations; a workload to generate the candidate ML project in the first phase is determined; a performance of the candidate ML project in the first phase is determined; an additional workload to generate the candidate ML project in the second phase is determined; and an increased performance of the candidate ML project in the second phase is determined. Final ML projects are selected from the candidate ML projects, based on the strategy. Artifacts of the final ML projects are incorporated into the new ML project.

Abstract: A computer-implemented method includes receiving a strategy associated with a new machine-learning (ML) project. There exist a plurality of ML projects, each of which includes artifacts, and for each such candidate project, the following are performed: iterations of the candidate ML project are divided into a first phase, including a first set of iterations, and a second phase, including a second set of iterations; a workload to generate the candidate ML project in the first phase is determined; a performance of the candidate ML project in the first phase is determined; an additional workload to generate the candidate ML project in the second phase is determined; and an increased performance of the candidate ML project in the second phase is determined. Final ML projects are selected from the candidate ML projects, based on the strategy. Artifacts of the final ML projects are incorporated into the new ML project.

Abstract: The present invention relates to machine vision computing environments, and more specifically relates to a system and method for selectively accelerating the execution of image processing applications using a multi-core processor system. To this extent, a multi-core processor system is generally defined as one that is multi-platform, and potentially distributed via a network or other connection. The invention provides a machine vision system and method for executing image processing applications referred to herein as an image co-processor that comprises (among other things) a plurality of multi-core processors (MCPs) that work to process multiple images in an accelerated fashion.

Abstract: The present invention relates to machine vision computing environments, and more specifically relates to a system and method for selectively accelerating the execution of image processing applications using a hybrid computing system. To this extent, a hybrid system is generally defined as one that is multi-platform, and potentially distributed via a network or other connection. The invention provides a machine vision system and method for executing image processing applications on a hybrid image processing system referred to herein as an image co-processor that comprises (among other things) a plurality of special purpose engines (SPEs) that work to process multiple images in an accelerated fashion.

Abstract: This is an embodiment for enabling calibration of the bus interfacing cell processors and I/O Controllers in a multi-cell system without rebooting the system in response to a change in the environment temperature. This is accomplished by periodically checking the intake temperature. If the temperature rise is less than a predefined threshold, no action is taken. If the temperature rise is more than a predefined threshold, external interfaces are disabled, cell operations are halted and calibration is performed. Once the calibration is completed, cell operations are resumed and external interfaces are re-enabled.

Abstract: The present invention relates to machine vision computing environments, and more specifically relates to a system and method for selectively accelerating the execution of image processing applications using a multi-core processor system. To this extent, a multi-core processor system is generally defined as one that is multi-platform, and potentially distributed via a network or other connection. The invention provides a machine vision system and method for executing image processing applications referred to herein as an image co-processor that comprises (among other things) a plurality of multi-core processors (MCPs) that work to process multiple images in an accelerated fashion.

Abstract: The present invention relates to machine vision computing environments, and more specifically relates to a system and method for selectively accelerating the execution of image processing applications using a hybrid computing system. To this extent, a hybrid system is generally defined as one that is multi-platform, and potentially distributed via a network or other connection. The invention provides a machine vision system and method for executing image processing applications on a hybrid image processing system referred to herein as an image co-processor that comprises (among other things) a plurality of special purpose engines (SPEs) that work to process multiple images in an accelerated fashion.

Abstract: A first array of disk drives overlaps with a second array of disk drives in a Redundant Array of Inexpensive Drives (RAID) system, in which the first and second arrays share at least one disk drive. A first stripe of data from a first client is stored in the first array, and a second stripe of data from a second client is stored in the second array. The shared disk drives are less than the number of drives needed to reconstruct a full stripe. Thus, in the event of a drive failure in the first array, the first client can reconstruct the first data stripe, but is never able to reconstruct the second stripe. Likewise, in the event of a drive failure in the second array, the second client can reconstruct the second data stripe, but is never able to reconstruct the first stripe.

Abstract: A first array of disk drives overlaps with a second array of disk drives in a Redundant Array of Inexpensive Drives (RAID) system, in which the first and second arrays share at least one disk drive. A first stripe of data from a first client is stored in the first array, and a second stripe of data from a second client is stored in the second array. The shared disk drives are less than the number of drives needed to reconstruct a full stripe. Thus, in the event of a drive failure in the first array, the first client can reconstruct the first data stripe, but is never able to reconstruct the second stripe. Likewise, in the event of a drive failure in the second array, the second client can reconstruct the second data stripe, but is never able to reconstruct the first stripe.

Abstract: A first array of disk drives overlaps with a second array of disk drives in a Redundant Array of Inexpensive Drives (RAID) system, in which the first and second arrays share at least one disk drive. A first stripe of data from a first client is stored in the first array, and a second stripe of data from a second client is stored in the second array. The shared disk drives are less than the number of drives needed to reconstruct a full stripe. Thus, in the event of a drive failure in the first array, the first client can reconstruct the first data stripe, but is never able to reconstruct the second stripe. Likewise, in the event of a drive failure in the second array, the second client can reconstruct the second data stripe, but is never able to reconstruct the first stripe.