Abstract: A system for processing spatial data may be designed to receive neural network outputs corresponding to a first spatial data set, and translate the neural network outputs corresponding to the first spatial data set based on the motion between a second spatial data set and the first spatial data set. The system may perform zero-gap run length encoding on the neural network outputs to store the neural network outputs in memory. The system may also perform on-the-fly skip zero decoding and bilinear interpolation to translate the neural network outputs.

Abstract: A system for processing spatial data may be designed to receive neural network outputs corresponding to a first spatial data set, and translate the neural network outputs corresponding to the first spatial data set based on the motion between a second spatial data set and the first spatial data set. The system may perform zero-gap run length encoding on the neural network outputs to store the neural network outputs in memory. The system may also perform on-the-fly skip zero decoding and bilinear interpolation to translate the neural network outputs.

Abstract: A configurable image processing system can process image data for multiple applications by including an image sensor capable of operating in a machine vision mode and a photography mode in response to an operating system command. When operating in machine vision mode, the image sensor may send image data to first processor for machine vision processing. When operating in photography mode, the image sensor may send image data to an image coprocessor for photography processing.

Abstract: A configurable image processing system can process image data for multiple applications by including an image sensor capable of operating in a machine vision mode and a photography mode in response to an operating system command. When operating in machine vision mode, the image sensor may send image data to first processor for machine vision processing. When operating in photography mode, the image sensor may send image data to an image coprocessor for photography processing.

Abstract: Various techniques for evaluating probabilistic assertions are described herein. In one example, a method includes transforming a program, a probabilistic assertion, and an input into an intermediate representation, the intermediate representation including a Bayesian network of nodes representing distributions. The method further includes verifying a probabilistic assertion in the program using the intermediate representation.

Abstract: The present technology relaxes the precision (or full data-correctness-guarantees) requirements in memory operations, such as writing or reading, of MLC memories so that an application may write and read a digital data value as an approximate value. Types of MLCs include Flash MLC and MLC Phase Change Memory (PCM) as well as other resistive technologies. Many software applications may not need the accuracy or precision typically used to store and read data values. For example, an application may render an image on a relatively low resolution display and may not need an accurate data value for each pixel. By relaxing the precision or correctness requirements is a memory operation, MLC memories may have increased performance, lifetime, density, and/or energy efficiency.

Abstract: Various techniques for evaluating probabilistic assertions are described herein. In one example, a method includes transforming a program, a probabilistic assertion, and an input into an intermediate representation, the intermediate representation including a Bayesian network of nodes representing distributions. The method further includes verifying a probabilistic assertion in the program using the intermediate representation.

Abstract: A system and method are provided for enhancing approximate computing by a computer system. In one example, an interface is provided comprising a variable-identifier module and a bit-priority module. The variable-identifier module is configured to identify one or more variables of data that are to be processed by the computer system with approximate precision. Approximate precision is a precision level at which a hardware device does not guarantee full data-correctness for the one or more variables. The bit-priority module is configured to assign bit-priorities to the one or more variables. The bit-priorities include relative levels of importance among bits of each of the one or more variables. The relative levels of importance include at least high-priority bits and low-priority bits.

Abstract: The present technology relaxes the precision (or full data-correctness-guarantees) requirements in memory operations, such as writing or reading, of MLC memories so that an application may write and read a digital data value as an approximate value. Types of MLCs include Flash MLC and MLC Phase Change Memory (PCM) as well as other resistive technologies. Many software applications may not need the accuracy or precision typically used to store and read data values. For example, an application may render an image on a relatively low resolution display and may not need an accurate data value for each pixel. By relaxing the precision or correctness requirements is a memory operation, MLC memories may have increased performance, lifetime, density, and/or energy efficiency.

Abstract: The present technology relaxes the precision (or full data-correctness-guarantees) requirements in memory operations, such as writing or reading, of MLC memories so that an application may write and read a digital data value as an approximate value. Types of MLCs include Flash MLC and MLC Phase Change Memory (PCM) as well as other resistive technologies. Many software applications may not need the accuracy or precision typically used to store and read data values. For example, an application may render an image on a relatively low resolution display and may not need an accurate data value for each pixel. By relaxing the precision or correctness requirements is a memory operation, MLC memories may have increased performance, lifetime, density, and/or energy efficiency.

Abstract: The present technology relaxes the precision (or full data-correctness-guarantees) requirements in memory operations, such as writing or reading, of MLC memories so that an application may write and read a digital data value as an approximate value. Types of MLCs include Flash MLC and MLC Phase Change Memory (PCM) as well as other resistive technologies. Many software applications may not need the accuracy or precision typically used to store and read data values. For example, an application may render an image on a relatively low resolution display and may not need an accurate data value for each pixel. By relaxing the precision or correctness requirements is a memory operation, MLC memories may have increased performance, lifetime, density, and/or energy efficiency.

Abstract: A system and method are provided for enhancing approximate computing by a computer system. In one example, an interface is provided comprising a variable-identifier module and a bit-priority module. The variable-identifier module is configured to identify one or more variables of data that are to be processed by the computer system with approximate precision. Approximate precision is a precision level at which a hardware device does not guarantee full data-correctness for the one or more variables. The bit-priority module is configured to assign bit-priorities to the one or more variables. The bit-priorities include relative levels of importance among bits of each of the one or more variables. The relative levels of importance include at least high-priority bits and low-priority bits.