Abstract:
Methods and apparatus are described for simultaneously buffering and reformatting (e.g., transposing) a matrix for high-speed data streaming in general matrix multiplication (GEMM), which may be implemented by a programmable integrated circuit (IC). Examples of the present disclosure increase the effective double data rate (DDR) memory throughput for streaming data into GEMM digital signal processing (DSP) engine multifold, as well as eliminate slow data reformatting on a host central processing unit (CPU). This may be accomplished through software-defined (e.g., C++) data structures and access patterns that result in hardware logic that simultaneously buffers and reorganizes the data to achieve linear DDR addressing.
Abstract:
Detecting objects in video may include receiving object detections for a plurality of selected frames of a video from a still image detector, wherein the plurality of selected frames are non-adjacent frames of the video, propagating the object detections from the plurality of selected frames to sequential frames of the video adjacent to the plurality of selected frames based on a distance metric and vector flow data for the sequential frames, suppressing false positive object detections from the propagating, and outputting resulting object detections for the sequential frames of the video.
Abstract:
Implementing a circuit design can include detecting, using computer hardware, a re-convergent section of a circuit design, masking, using the computer hardware, a sequential circuit element of the re-convergent section located between a start and an end of the re-convergent section, and performing, using the computer hardware, an optimization operation on combinatorial logic of the re-convergent section to create optimized combinatorial logic. Using the computer hardware, the optimized combinatorial logic of the re-convergent section can be mapped. Further, the re-convergent section can be modified subsequent to the mapping to match timing of the re-convergent section prior to the masking.
Abstract:
An example preprocessor circuit for formatting image data into a plurality of streams of image samples includes: a plurality of memory banks configured to store the image data; multiplexer circuitry coupled to the memory banks; a first plurality of registers coupled to the multiplexer circuitry; a second plurality of registers coupled to the first plurality of registers, outputs of the second plurality of registers configured to provide the plurality of streams of image samples; bank address and control circuitry coupled to control inputs of the plurality of memory banks, the multiplexer circuitry, and the first plurality of registers; output control circuitry coupled to control inputs of the second plurality of registers; and a control state machine coupled to the bank address and control circuitry and the output control circuitry.
Abstract:
An example multiply accumulate (MACC) circuit includes: a multiply-accumulator having an accumulator output register; a quantizer, coupled to the multiply accumulator; and a control circuit coupled to the multiply-accumulator and the quantizer, the control circuit configured to provide control data to the quantizer, the control data indicative of a most-significant bit (MSB) to least significant bit (LSB) range for selecting bit indices from the accumulator output register.
Abstract:
Circuits and method for multiplying floating point operands. An exponent adder circuit sums a first exponent and a second exponent and generates an output exponent. A mantissa multiplier circuit multiplies a first mantissa and a second mantissa and generates an output mantissa. A first conversion circuit converts the output exponent and output mantissa into a fixed point number. An accumulator circuit sums contents of an accumulation register and the fixed point number into an accumulated value and stores the accumulated value in the accumulation register.
Abstract:
A disclosed neural network processing system includes a host computer system, a RAMs coupled to the host computer system, and neural network accelerators coupled to the RAMs, respectively. The host computer system is configured with software that when executed causes the host computer system to write input data and work requests to the RAMS. Each work request specifies a subset of neural network operations to perform and memory locations in a RAM of the input data and parameters. A graph of dependencies among neural network operations is built and additional dependencies added. The operations are partitioned into coarse grain tasks and fine grain subtasks for optimal scheduling for parallel execution. The subtasks are scheduled to accelerator kernels of matching capabilities. Each neural network accelerator is configured to read a work request from the respective RAM and perform the subset of neural network operations on the input data using the parameters.
Abstract:
In the disclosed methods and systems for processing in a neural network system, a host computer system writes a plurality of weight matrices associated with a plurality of layers of a neural network to a memory shared with a neural network accelerator. The host computer system further assembles a plurality of per-layer instructions into an instruction package. Each per-layer instruction specifies processing of a respective layer of the plurality of layers of the neural network, and respective offsets of weight matrices in a shared memory. The host computer system writes input data and the instruction package to the shared memory. The neural network accelerator reads the instruction package from the shared memory and processes the plurality of per-layer instructions of the instruction package.
Abstract:
A circuit for enabling a modification of an input data stream is described. The circuit comprises a first plurality of registers coupled in series; an input register of the first plurality of registers coupled to receive the input data stream; an output register of the first plurality of registers positioned at an end of the first plurality of registers; and a control circuit enabling a data value which is independent of the input data stream to be generated as an output of the circuit at a predetermined time.
Abstract:
Processing a circuit design includes generating a transformation output from a transformation input for each of a plurality of transformations of a synthesis flow applied to the circuit design. For each transformation, the transformation input and the transformation output represent the circuit design. At least one circuit element is changed from the transformation input to the transformation output. For each transformation, a hardware description language representation of the transformation input and a hardware description language representation of the transformation output are generated. For each transformation, determining whether the hardware description language representation of the transformation input is equivalent to the hardware description language representation of the transformation output.