Software-based high-level synthesis design of FPGA beamformers for synthetic aperture imaging PROJECT TITLE :Software-based high-level synthesis design of FPGA beamformers for synthetic aperture imagingABSTRACT:Field-programmable gate arrays (FPGAs) can potentially be configured as beamforming platforms for ultrasound imaging, but an extended style time and skilled experience in hardware programming are usually needed. In this article, we have a tendency to present a novel approach to the economical style of FPGA beamformers for artificial aperture (SA) imaging via the use of software-primarily based high-level synthesis techniques. Software kernels (coded in OpenCL) were first developed to stage-wise handle SA beamforming operations, and their corresponding FPGA logic circuitry was emulated through a high-level synthesis framework. Once design area analysis, the fine-tuned OpenCL kernels were compiled into register transfer level descriptions to configure an FPGA as a beamformer module. The processing performance of this beamformer was assessed through a series of offline emulation experiments that sought to derive beamformed pictures from SA channel-domain raw data (40-MHz sampling rate, 12 bit resolution). With 128 channels, our FPGA-based SA beamformer can achieve 41 frames per second (fps) processing throughput (3.44 ?? 108 pixels per second for frame size of 256 ?? 256 pixels) at 31.five W power consumption (one.30 fps/W power potency). It used eighty six.ninepercent of the FPGA material and operated at a 196.five MHz clock frequency (when optimization). Based mostly on these findings, we have a tendency to anticipate that FPGA and high-level synthesis will along foster speedy prototyping of real-time ultrasound processor modules at low power consumption budgets. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Autism Blogs: Expressed Emotion, Language Styles and Concerns in Personal and Community Settings Extended Kalman Filter-Based Parallel Dynamic State Estimation