Abstract: Methods, apparatus, systems, and articles of manufacture to optimize pipeline execution are disclosed. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to determine a value associated with a first location of a first pixel of a first image and a second location of a second pixel of a second image by calculating a matching cost between the first location and the second location, generate a disparity map including the value, and determine a minimum value based on the disparity map corresponding to a difference in horizontal coordinates between the first location and the second location.

Abstract: The present application discloses a computing device that can provide a low-power, highly capable computing platform for computational imaging. The computing device can include one or more processing units, for example one or more vector processors and one or more hardware accelerators, an intelligent memory fabric, a peripheral device, and a power management module. The computing device can communicate with external devices, such as one or more image sensors, an accelerometer, a gyroscope, or any other suitable sensor devices.

Abstract: Methods, systems, apparatus, and articles of manufacture to reduce memory latency when fetching pixel kernels are disclosed. An example apparatus includes first interface circuitry to receive a first request from a hardware accelerator at a first time including first coordinates of a first pixel disposed in a first image block, second interface circuitry to receive a second request including second coordinates from the hardware accelerator at a second time after the first time, and kernel retriever circuitry to, in response to the second request, determine whether the first image block is in cache storage based on a mapping of the second coordinates to a block tag, and, in response to determining that the first image block is in the cache storage, access, in parallel, two or more memory devices associated with the cache storage to transfer a plurality of image blocks including the first image block to the hardware accelerator.

Abstract: Methods, apparatus, systems, and articles of manufacture to optimize pipeline execution are disclosed. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to determine a value associated with a first location of a first pixel of a first image and a second location of a second pixel of a second image by calculating a matching cost between the first location and the second location, generate a disparity map including the value, and determine a minimum value based on the disparity map corresponding to a difference in horizontal coordinates between the first location and the second location.

Abstract: Technology for estimating neural network (NN) power profiles includes obtaining a plurality of workloads for a compiled NN model, the plurality of workloads determined for a hardware execution device, determining a hardware efficiency factor for the compiled NN model, and generating, based on the hardware efficiency factor, a power profile for the compiled NN model on one or more of a per-layer basis or a per-workload basis. The hardware efficiency factor can be determined on based on a hardware efficiency measurement and a hardware utilization measurement, and can be determined on a per-workload basis. A configuration file can be provided for generating the power profile, and an output visualization of the power profile can be generated. Further, feedback information can be generated to perform one or more of selecting a hardware device, optimizing a breakdown of workloads, optimizing a scheduling of tasks, or confirming a hardware device design.

Abstract: Systems, apparatuses and methods may provide for technology that determines a complexity of a task associated with a neural network workload and generates a hardware efficiency estimate for the task, wherein the hardware efficiency estimate is generated via a neural network based cost model if the complexity exceeds a threshold, and wherein the hardware efficiency estimate is generated via a cost function if the complexity does not exceed the threshold. In one example, the technology trains the neural network based cost model based on one or more of hardware profile data or register-transfer level (RTL) data.

Abstract: Methods, apparatus, systems, and articles of manufacture to optimize pipeline execution are disclosed. An example apparatus includes a cost computation manager to determine a value associated with a first location of a first pixel of a first image and a second location of a second pixel of a second image by calculating a matching cost between the first location and the second location, and an aggregation generator to generate a disparity map including the value, and determine a minimum value based on the disparity map corresponding to a difference in horizontal coordinates between the first location and the second location.

Abstract: Methods, systems, apparatus, and articles of manufacture to reduce memory latency when fetching pixel kernels are disclosed. An example apparatus includes first interface circuitry to receive a first request from a hardware accelerator at a first time including first coordinates of a first pixel disposed in a first image block, second interface circuitry to receive a second request including second coordinates from the hardware accelerator at a second time after the first time, and kernel retriever circuitry to, in response to the second request, determine whether the first image block is in cache storage based on a mapping of the second coordinates to a block tag, and, in response to determining that the first image block is in the cache storage, access, in parallel, two or more memory devices associated with the cache storage to transfer a plurality of image blocks including the first image block to the hardware accelerator.

Abstract: The present application discloses a computing device that can provide a low-power, highly capable computing platform for computational imaging. The computing device can include one or more processing units, for example one or more vector processors and one or more hardware accelerators, an intelligent memory fabric, a peripheral device, and a power management module. The computing device can communicate with external devices, such as one or more image sensors, an accelerometer, a gyroscope, or any other suitable sensor devices.

Abstract: The present application discloses a computing device that can provide a low-power, highly capable computing platform for computational imaging. The computing device can include one or more processing units, for example one or more vector processors and one or more hardware accelerators, an intelligent memory fabric, a peripheral device, and a power management module. The computing device can communicate with external devices, such as one or more image sensors, an accelerometer, a gyroscope, or any other suitable sensor devices.

Abstract: Methods, systems, apparatus, and articles of manufacture to reduce memory latency when fetching pixel kernels are disclosed. An example apparatus includes a prefetch kernel retriever to generate a block tag based on a first request from a hardware accelerator, the first request including first coordinates of a first pixel disposed in a first image block, a memory interface engine to store the first image block including a plurality of pixels including the pixel in a cache storage based on the block tag, and a kernel retriever to access two or more memory devices included in the cache storage in parallel to transfer a plurality of image blocks including the first image block when a second request is received including second coordinates of a second pixel disposed in the first image block.

Abstract: The present application discloses a computing device that can provide a low-power, highly capable computing platform for computational imaging. The computing device can include one or more processing units, for example one or more vector processors and one or more hardware accelerators, an intelligent memory fabric, a peripheral device, and a power management module. The computing device can communicate with external devices, such as one or more image sensors, an accelerometer, a gyroscope, or any other suitable sensor devices.

Abstract: Methods, apparatus, systems, and articles of manufacture to optimize pipeline execution are disclosed. An example apparatus includes a cost computation manager to determine a value associated with a first location of a first pixel of a first image and a second location of a second pixel of a second image by calculating a matching cost between the first location and the second location, and an aggregation generator to generate a disparity map including the value, and determine a minimum value based on the disparity map corresponding to a difference in horizontal coordinates between the first location and the second location.

Abstract: Methods, systems, apparatus, and articles of manufacture to reduce memory latency when fetching pixel kernels are disclosed. An example apparatus includes a prefetch kernel retriever to generate a block tag based on a first request from a hardware accelerator, the first request including first coordinates of a first pixel disposed in a first image block, a memory interface engine to store the first image block including a plurality of pixels including the pixel in a cache storage based on the block tag, and a kernel retriever to access two or more memory devices included in the cache storage in parallel to transfer a plurality of image blocks including the first image block when a second request is received including second coordinates of a second pixel disposed in the first image block.

Abstract: Cache memory mapping techniques are presented. A cache may contain an index configuration register. The register may configure the locations of an upper index portion and a lower index portion of a memory address. The portions may be combined to create a combined index. The configurable split-index addressing structure may be used, among other applications, to reduce the rate of cache conflicts occurring between multiple processors decoding the video frame in parallel.

Abstract: The present application discloses a computing device that can provide a low-power, highly capable computing platform for computational imaging. The computing device can include one or more processing units, for example one or more vector processors and one or more hardware accelerators, an intelligent memory fabric, a peripheral device, and a power management module. The computing device can communicate with external devices, such as one or more image sensors, an accelerometer, a gyroscope, or any other suitable sensor devices.

Abstract: The present application relates generally to a parallel processing device. The parallel processing device can include a plurality of processing elements, a memory subsystem, and an interconnect system. The memory subsystem can include a plurality of memory slices, at least one of which is associated with one of the plurality of processing elements and comprises a plurality of random access memory (RAM) tiles, each tile having individual read and write ports. The interconnect system is configured to couple the plurality of processing elements and the memory subsystem. The interconnect system includes a local interconnect and a global interconnect.

Abstract: Cache memory mapping techniques are presented. A cache may contain an index configuration register. The register may configure the locations of an upper index portion and a lower index portion of a memory address. The portions may be combined to create a combined index. The configurable split-index addressing structure may be used, among other applications, to reduce the rate of cache conflicts occurring between multiple processors decoding the video frame in parallel.

Abstract: The present application relates generally to a parallel processing device. The parallel processing device can include a plurality of processing elements, a memory subsystem, and an interconnect system. The memory subsystem can include a plurality of memory slices, at least one of which is associated with one of the plurality of processing elements and comprises a plurality of random access memory (RAM) tiles, each tile having individual read and write ports. The interconnect system is configured to couple the plurality of processing elements and the memory subsystem. The interconnect system includes a local interconnect and a global interconnect.

Abstract: Cache memory mapping techniques are presented. A cache may contain an index configuration register. The register may configure the locations of an upper index portion and a lower index portion of a memory address. The portions may be combined to create a combined index. The configurable split-index addressing structure may be used, among other applications, to reduce the rate of cache conflicts occurring between multiple processors decoding the video frame in parallel.