Abstract:
A system for power measurement in an electronic device includes a sensing unit, an analog-to-digital converter (ADC) and a controller. The sensing unit senses voltage across a power source and modulates a carrier signal based on the sensed voltage. The ADC converts a combination of the modulated carrier signal and audio signals received by the electronic device to generate a digitized combined signal and provides the digitized combined signal to the controller. The controller separates digitized modulated carrier signal and digitized audio signals. The digitized modulated carrier signal is demodulated to generate an output signal that provides a measure of the power consumed by the electronic device.
Abstract:
Parallelization of decoding of a data stream encoded with a variable length code includes determining one or more markers, each of which indicates a position within the encoded data stream. The determined markers are included into the encoded data stream together with the encoded data. At the decoder side, the markers are parsed from the encoded data stream and based on the extracted markers. The encoded data is separated into partitions, which are decoded separately and in parallel.
Abstract:
An image processing arrangement includes an input to receive an indicator of a power characteristic related to an image processing arrangement and an image processor to process an image based on the indicator of the power characteristic.
Abstract:
An integrated circuit includes a reconfigurable stream switch and an arithmetic circuit. The stream switch, in operation, streams data. The arithmetic circuit has a plurality of inputs coupled to the reconfigurable stream switch. In operation, the arithmetic circuit generates an output according to AX+BY+C, where A, B and C are vector or scalar constants, and X and Y are data streams streamed to the arithmetic circuit through the reconfigurable stream switch.
Abstract:
Embodiments are directed towards a system on chip (SoC) that implements a deep convolutional network heterogeneous architecture. The SoC includes a system bus, a plurality of addressable memory arrays coupled to the system bus, at least one applications processor core coupled to the system bus, and a configurable accelerator framework coupled to the system bus. The configurable accelerator framework is an image and deep convolutional neural network (DCNN) co-processing system. The SoC also includes a plurality of digital signal processors (DSPs) coupled to the system bus, wherein the plurality of DSPs coordinate functionality with the configurable accelerator framework to execute the DCNN.
Abstract:
Techniques and systems are provided for implementing a convolutional neural network. One or more convolution accelerators are provided that each include a feature line buffer memory, a kernel buffer memory, and a plurality of multiply-accumulate (MAC) circuits arranged to multiply and accumulate data. In a first operational mode the convolutional accelerator stores feature data in the feature line buffer memory and stores kernel data in the kernel data buffer memory. In a second mode of operation, the convolutional accelerator stores kernel decompression tables in the feature line buffer memory.
Abstract:
A memory array arranged as a plurality of memory cells. The memory cells are configured to operate at a determined voltage. A memory management circuitry coupled to the plurality of memory cells tags a first set of the plurality of memory cells as low-voltage cells and tags a second set of the plurality of memory cells as high-voltage cells. A power source provides a low voltage to the first set of memory cells and provides a high voltage to the second set of memory cells based on the tags.
Abstract:
A device include on-board memory, an applications processor, a digital signal processor (DSP) cluster, a configurable accelerator framework (CAF), and a communication bus architecture. The communication bus communicatively couples the applications processor, the DSP cluster, and the CAF to the on-board memory. The CAF includes a reconfigurable stream switch and data volume sculpting circuitry, which has an input and an output coupled to the reconfigurable stream switch. The data volume sculpting circuitry receives a series of frames, each frame formed as a two dimensional (2D) data structure, and determines a first dimension and a second dimension of each frame of the series of frames. Based on the first and second dimensions, the data volume sculpting circuitry determines for each frame a position and a size of a region-of-interest to be extracted from the respective frame, and extracts from each frame, data in the frame that is within the region-of-interest.
Abstract:
Embodiments of an electronic device include an integrated circuit, a reconfigurable stream switch formed in the integrated circuit along with a plurality of convolution accelerators and a decompression unit coupled to the reconfigurable stream switch. The decompression unit decompresses encoded kernel data in real time during operation of convolutional neural network.
Abstract:
A device include on-board memory, an applications processor, a digital signal processor (DSP) cluster, a configurable accelerator framework (CAF), and at least one communication bus architecture. The communication bus communicatively couples the applications processor, the DSP cluster, and the CAF to the on-board memory. The CAF includes a reconfigurable stream switch and data volume sculpting circuitry, which has an input and an output coupled to the reconfigurable stream switch. The data volume sculpting circuitry receives a series of frames, each frame formed as a two dimensional (2D) data structure, and determines a first dimension and a second dimension of each frame of the series of frames. Based on the first and second dimensions, the data volume sculpting circuitry determines for each frame a position and a size of a region-of-interest to be extracted from the respective frame, and extracts from each frame, data in the frame that is within the region-of-interest.