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 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: 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: In the parallel operation of power supply units, a high line ripple current may be observed in output when the power supply units (PSUs) are supplied with different inputs. For example, a high line ripple current may be observed when PSUs were supplied with different line frequency inputs and/or when PSUs were supplied with different phase angle input lines. A low pass filter is in a control loop which is capable of filtering the line frequency to get an average current reference signal. The average current reference signal is compared with the real time output current to generate an error signal. This error signal is fed back to a voltage control loop to adjust the output in order to compensate the line ripple.

Abstract: A vector processor is disclosed including a variety of variable-length instructions. Computer-implemented methods are disclosed for efficiently carrying out a variety of operations in a time-conscious, memory-efficient, and power-efficient manner. Methods for more efficiently managing a buffer by controlling the threshold based on the length of delay line instructions are disclosed. Methods for disposing multi-type and multi-size operations in hardware are disclosed. Methods for condensing look-up tables are disclosed. Methods for in-line alteration of variables are disclosed.

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: Methods, apparatus, systems, and articles of manufacture are disclosed to improve convolution efficiency of a convolution neural network (CNN) accelerator. An example hardware accelerator includes a hardware data path element (DPE) in a DPE array, the hardware DPE including an accumulator, and a multiplier coupled to the accumulator, the multiplier to multiply first inputs including an activation value and a filter coefficient value to generate a first convolution output when the hardware DPE is in a convolution mode, and a controller coupled to the DPE array, the controller to adjust the hardware DPE from the convolution mode to a pooling mode by causing at least one of the multiplier or the accumulator to generate a second convolution output based on second inputs, the second inputs including an output location value of a pool area, at least one of the first inputs different from at least one of the second inputs.

Abstract: The present application provides a method of corner detection and an image processing system for detecting corners in an image. The preferred implementation is in software using enabling and reusable hardware features in the underlying vector processor architecture. The advantage of this combined software and programmable processor datapath hardware is that the same hardware used for the FAST algorithm can also be readily applied to a variety of other computational tasks, not limited to image processing.

Abstract: A vector processor is disclosed including a variety of variable-length instructions. Computer-implemented methods are disclosed for efficiently carrying out a variety of operations in a time-conscious, memory-efficient, and power-efficient manner. Methods for more efficiently managing a buffer by controlling the threshold based on the length of delay line instructions are disclosed. Methods for disposing multi-type and multi-size operations in hardware are disclosed. Methods for condensing look-up tables are disclosed. Methods for in-line alteration of variables are disclosed.

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: In the parallel operation of power supply units, a high line ripple current may be observed in output when the power supply units (PSUs) are supplied with different inputs. For example, a high line ripple current may be observed when PSUs were supplied with different line frequency inputs and/or when PSUs were supplied with different phase angle input lines. A low pass filter is in a control loop which is capable of filtering the line frequency to get an average current reference signal. The average current reference signal is compared with the real time output current to generate an error signal. This error signal is fed back to a voltage control loop to adjust the output in order to compensate the line ripple.

Abstract: Methods, systems, apparatus and articles of manufacture to identify features within an image are disclosed herein. An example apparatus includes a horizontal cost (HCOST) engine to apply a first row of pixels of a macroblock to an input of a first HCOST unit, the first HCOST unit including a number of difference calculators; and a difference calculator engine to apply corresponding rows of pixels of a search window of a source image to corresponding ones of the number of difference calculators of the first HCOST unit, the corresponding ones of the number of difference calculators to calculate respective sums of absolute difference (SAD) values between (a) the first row of pixels of the macroblock and (b) the corresponding rows of pixels of the search window.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve convolution efficiency of a convolution neural network (CNN) accelerator. An example hardware accelerator includes a hardware data path element (DPE) in a DPE array, the hardware DPE including an accumulator, and a multiplier coupled to the accumulator, the multiplier to multiply first inputs including an activation value and a filter coefficient value to generate a first convolution output when the hardware DPE is in a convolution mode, and a controller coupled to the DPE array, the controller to adjust the hardware DPE from the convolution mode to a pooling mode by causing at least one of the multiplier or the accumulator to generate a second convolution output based on second inputs, the second inputs including an output location value of a pool area, at least one of the first inputs different from at least one of the second inputs.

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: The present application provides a method of corner detection and an image processing system for detecting corners in an image. The preferred implementation is in software using enabling and reusable hardware features in the underlying vector processor architecture. The advantage of this combined software and programmable processor datapath hardware is that the same hardware used for the FAST algorithm can also be readily applied to a variety of other computational tasks, not limited to image processing.

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: Methods, systems, apparatus and articles of manufacture to identify features within an image are disclosed herein. An example apparatus includes a horizontal cost (HCOST) engine to apply a first row of pixels of a macroblock to an input of a first HCOST unit, the first HCOST unit including a number of difference calculators; and a difference calculator engine to apply corresponding rows of pixels of a search window of a source image to corresponding ones of the number of difference calculators of the first HCOST unit, the corresponding ones of the number of difference calculators to calculate respective sums of absolute difference (SAD) values between (a) the first row of pixels of the macroblock and (b) the corresponding rows of pixels of the search window.

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 provides a method of corner detection and an image processing system for detecting corners in an image. The preferred implementation is in software using enabling and reusable hardware features in the underlying vector processor architecture. The advantage of this combined software and programmable processor datapath hardware is that the same hardware used for the FAST algorithm can also be readily applied to a variety of other computational tasks, not limited to image processing.