Abstract: A stream switch includes a data router, configuration registers, and arbitration logic. The data router has a plurality of input ports, each having a plurality of associated virtual input channels, and a plurality of output ports, each having a plurality of associated virtual output channels. The data router transmits data streams from input ports to one or more output ports of the plurality of output ports. The configuration registers store configuration data associated with the virtual output channels of the respective output ports of the plurality of output ports. The stored configuration data identifies a source input port and virtual input channel ID associated with the virtual output channel of the output port. The arbitration logic allocates bandwidth of the data router based on request signals associated with virtual input channels of the input ports and the configuration data associated with the virtual output channels.

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: A neural network includes an internal storage unit. The internal storage unit stores feature data received from a memory external to the neural network. The internal storage unit reads the feature data to a hardware accelerator of the neural network. The internal storage unit adapts a storage pattern of the feature data and a read pattern of the feature data to enhance the efficiency of the hardware accelerator.

Abstract: A neural network includes an internal storage unit. The internal storage unit stores feature data received from a memory external to the neural network. The internal storage unit reads the feature data to a hardware accelerator of the neural network. The internal storage unit adapts a storage pattern of the feature data and a read pattern of the feature data to enhance the efficiency of the hardware accelerator.

Abstract: A neural network is able to reconfigure hardware accelerators on-the-fly without stopping downstream hardware accelerators. The neural network inserts a reconfiguration tag into the stream of feature data. If the reconfiguration tag matches an identification of a hardware accelerator, a reconfiguration process is initiated. Upstream hardware accelerators are paused while downstream hardware accelerators continue to operate. An epoch controller reconfigures the hardware accelerator via a bus. Normal operation of the neural network then resumes.

Abstract: A convolutional accelerator framework (CAF) has a plurality of processing circuits including one or more convolution accelerators, a reconfigurable hardware buffer configurable to store data of a variable number of input data channels, and a stream switch coupled to the plurality of processing circuits. The reconfigurable hardware buffer has a memory and control circuitry. A number of the variable number of input data channels is associated with an execution epoch. The stream switch streams data of the variable number of input data channels between processing circuits of the plurality of processing circuits and the reconfigurable hardware buffer during processing of the execution epoch. The control circuitry of the reconfigurable hardware buffer configures the memory to store data of the variable number of input data channels, the configuring including allocating a portion of the memory to each of the variable number of input data channels.

Abstract: A method comprising: setting up a stationary surveying device at a first known positioning in a surrounding area of the object; retrieving from a memory a set of object points of an object to be surveyed and/or to be marked; surveying and/or marking from a first positioning object points of the set of object points that can be surveyed and/or can be marked from the first positioning by means of the free beam, on the basis of a target direction; ascertaining missing object points of a set of object points; relocating the surveying device to a second, unknown positioning in the surrounding area of the object; automatically determining a second positioning by the surveying device on the basis of the knowledge of the first positioning, so that the second positioning is known; surveying and/or marking missing object points by means of the free beam from the second positioning.

Abstract: A method comprising: setting up a stationary surveying device at a first known positioning in a surrounding area of the object; retrieving from a memory a set of object points of an object to be surveyed and/or to be marked; surveying and/or marking from a first positioning object points of the set of object points that can be surveyed and/or can be marked from the first positioning by means of the free beam, on the basis of a target direction; ascertaining missing object points of a set of object points; relocating the surveying device to a second, unknown positioning in the surrounding area of the object; automatically determining a second positioning by the surveying device on the basis of the knowledge of the first positioning, so that the second positioning is known; surveying and/or marking missing object points by means of the free beam from the second positioning.

Abstract: An auxiliary measuring instrument, configured to form together with a ground-based surveying device having range-and-direction measuring functionality, a system for surveying or staking out object points, wherein the auxiliary measuring instrument including a handheld main body of a defined length, and a man-machine interface, wherein the auxiliary measuring instrument is designed in a pen-like form and size and is configured to aim at an object point to be surveyed or staked out in a one-handed manner with a first end of the auxiliary measuring instrument and wherein a body is attached at a second end of the auxiliary measuring instrument, wherein the body is designed for optical-image-based determination of the position of the auxiliary measuring instrument by the surveying device.

Abstract: Embodiments are directed towards a hardware accelerator engine that supports efficient mapping of convolutional stages of deep neural network algorithms. The hardware accelerator engine includes a plurality of convolution accelerators, and each one of the plurality of convolution accelerators includes a kernel buffer, a feature line buffer, and a plurality of multiply-accumulate (MAC) units. The MAC units are arranged to multiply and accumulate data received from both the kernel buffer and the feature line buffer. The hardware accelerator engine also includes at least one input bus coupled to an output bus port of a stream switch, at least one output bus coupled to an input bus port of the stream switch, or at least one input bus and at least one output bus hard wired to respective output bus and input bus ports of the stream switch.

Abstract: A hardware accelerator includes processing elements of a neural network, each processing element having a memory; a stream switch; stream engines coupled to functional circuits via the stream switch, wherein the stream engines, in operation, generate data streaming requests to stream data to and from functional circuits of the plurality of functional circuits; a first system bus interface coupled to the stream engines; a second system bus interface coupled to the processing elements; and mode control circuitry, which, in operation, sets respective modes of operation for the plurality of processing elements. The modes of operation include: a compute mode of operation in which the processing element performs computing operations using the memory associated with the processing element; and a memory mode of operation in which the memory associated with the processing element performs memory operations, bypassing the stream switch, via the second system bus interface.

Abstract: A hardware accelerator includes a plurality of functional circuits, a stream switch, and a plurality of stream engines. The stream engines are coupled to the functional circuits via the stream switch, and in operation, generate data streaming requests to stream data to and from the functional circuits. The functional circuits include at least one convolutional cluster, which includes a plurality of processing elements coupled together via a reconfigurable crossbar switch. The reconfigurable crossbar switch is coupled to the stream switch, and in operation, streams data to, from, and between processing elements of the processing cluster.

Abstract: A hardware accelerator includes functional circuits and streaming engines. An interface is coupled to the plurality of streaming engines. The interface, in operation, performs stream cipher operations on data words associated with data streaming requests. The performing of a stream cipher operation on a data word includes generating a mask based on an encryption ID associated with a streaming engine of the plurality of streaming engines and an address associated with the data word, and XORing the generated mask with the data word. The hardware accelerator may include configuration registers to store configuration information indicating a respective security state associated with functional circuits and streaming engine of the hardware accelerator, which may be used to control performance of operations by the hardware accelerator.

Abstract: A system includes a host processor, a memory, a hardware accelerator and a configuration controller. The host processor, in operation, controls execution of a multi-stage processing task. The memory, in operation, stores data and configuration information. The hardware accelerator, in operation preforms operations associated with stages of the multi-stage processing task. The configuration controller is coupled to the host processor, the hardware accelerator, and the memory. The configuration controller executes a linked list of configuration operations, for example, under control of a finite state machine. The linked list consists of configuration operations selected from a defined set of configuration operations. Executing the linked list of configuration operations configures the plurality of configuration registers of the hardware accelerator to control operations of the hardware accelerator associated with a stage of the multi-stage processing task.

Abstract: A calibrating method and system for an auxiliary measuring instrument which is designed to form together with a ground-based, stationary, surveying device having range-and-direction measuring functionality, a total station, a system for surveying and/or staking out object points. The auxiliary measuring instrument including a body which has a code for determining the orientation by using a pivoting movement of the body about a resting contact end of the auxiliary measuring instrument.

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: Systems and devices are provided to increase computational and/or power efficiency for one or more neural networks via a computationally driven closed-loop dynamic clock control. A clock frequency control word is generated based on information indicative of a current frame execution rate of a processing task of the neural network and a reference clock signal. A clock generator generates the clock signal of neural network based on the clock frequency control word. A reference frequency may be used to generate the clock frequency control word, and the reference frequency may be based on information indicative of a sparsity of data of a training frame.

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: 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 convolutional accelerator includes a feature line buffer, a kernel buffer, a multiply-accumulate cluster, and iteration control circuitry. The convolutional accelerator, in operation, convolves a kernel with a streaming feature data tensor. The convolving includes decomposing the kernel into a plurality of sub-kernels and iteratively convolving the sub-kernels with respective sub-tensors of the streamed feature data tensor. The iteration control circuitry, in operation, defines respective windows of the streamed feature data tensors, the windows corresponding to the sub-tensors.