Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: A digital computer system automatically creates an Instruction Set Architecture (ISA) that potentially exploits VLIW instructions, vector operations, fused operations, and specialized operations with the goal of increasing the performance of a set of applications while keeping hardware cost below a designer specified limit, or with the goal of minimizing hardware cost given a required level of performance.

Abstract: An automated processor design tool uses a description of customized processor instruction set extensions in a standardized language to develop a configurable definition of a target instruction set, a Hardware Description Language description of circuitry necessary to implement the instruction set, and development tools such as a compiler, assembler, debugger and simulator which can be used to develop applications for the processor and to verify it. Implementation of the processor circuitry can be optimized for various criteria such as area, power consumption, speed and the like. Once a processor configuration is developed, it can be tested and inputs to the system modified to iteratively optimize the processor implementation. By providing a constrained domain of extensions and optimizations, the process can be automated to a high degree, thereby facilitating fast and reliable development.

Abstract: A system for generating processor hardware supports a language for significant extensions to the processor instruction set, where the designer specifies only the semantics of the new instructions and the system generates other logic. The extension language provides for the addition of processor state, including register files, and instructions that operate on that state. The language also provides for new data types to be added to the compiler to represent the state added. It allows separate specification of reference semantics and instruction implementation, and uses this to automate design verification. In addition, the system generates formatted instruction set documentation from the language specification.

Abstract: A digital computer system automatically creates an Instruction Set Architecture (ISA) that potentially exploits VLIW instructions, vector operations, fused operations, and specialized operations with the goal of increasing the performance of a set of applications while keeping hardware cost below a designer specified limit, or with the goal of minimizing hardware cost given a required level of performance.

Abstract: A digital computer system automatically creates an Instruction Set Architecture (ISA) that potentially exploits VLIW instructions, vector operations, fused operations, and specialized operations with the goal of increasing the performance of a set of applications while keeping hardware cost below a designer specified limit, or with the goal of minimizing hardware cost given a required level of performance.

Abstract: A digital computer system automatically creates an Instruction Set Architecture (ISA) that potentially exploits VLIW instructions, vector operations, fused operations, and specialized operations with the goal of increasing the performance of a set of applications while keeping hardware cost below a designer specified limit, or with the goal of minimizing hardware cost given a required level of performance.

Abstract: In a computer system, a method for determining an optimal loop interchange, set of register tiling amount, and cache tiling size for compiling source code into object code. The method first constructs a model of the specific computer system upon which the object code is to be run. Next, the search space comprising all of the different possibilities of the loop interchanges, register tiling amounts, and cache tiling sizes is run through the model to determine estimated times of execution. The particular loop interchange, set of register tiling amounts, and cache tiling sizes corresponding to the best estimated time of execution is then selected as being the most optimal. The source code is then compiled according to this optimal loop interchange, register tiling amount, and cache tiling size.