20,686 research outputs found
Evaluation of Single-Chip, Real-Time Tomographic Data Processing on FPGA - SoC Devices
A novel approach to tomographic data processing has been developed and
evaluated using the Jagiellonian PET (J-PET) scanner as an example. We propose
a system in which there is no need for powerful, local to the scanner
processing facility, capable to reconstruct images on the fly. Instead we
introduce a Field Programmable Gate Array (FPGA) System-on-Chip (SoC) platform
connected directly to data streams coming from the scanner, which can perform
event building, filtering, coincidence search and Region-Of-Response (ROR)
reconstruction by the programmable logic and visualization by the integrated
processors. The platform significantly reduces data volume converting raw data
to a list-mode representation, while generating visualization on the fly.Comment: IEEE Transactions on Medical Imaging, 17 May 201
Computer hardware and software for robotic control
The KSC has implemented an integrated system that coordinates state-of-the-art robotic subsystems. It is a sensor based real-time robotic control system performing operations beyond the capability of an off-the-shelf robot. The integrated system provides real-time closed loop adaptive path control of position and orientation of all six axes of a large robot; enables the implementation of a highly configurable, expandable testbed for sensor system development; and makes several smart distributed control subsystems (robot arm controller, process controller, graphics display, and vision tracking) appear as intelligent peripherals to a supervisory computer coordinating the overall systems
An improved artificial dendrite cell algorithm for abnormal signal detection
In dendrite cell algorithm (DCA), the abnormality of a data point is determined by comparing the multi-context antigen value (MCAV) with anomaly threshold. The limitation of the existing threshold is that the value needs to be determined before mining based on previous information and the existing MCAV is inefficient when exposed to extreme values. This causes the DCA fails to detect new data points if the pattern has distinct behavior from previous information and affects detection accuracy. This paper proposed an improved anomaly threshold solution for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability. In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV. From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy
Performance evaluation over HW/SW co-design SoC memory transfers for a CNN accelerator
Many FPGAs vendors have recently included embedded
processors in their devices, like Xilinx with ARM-Cortex
A cores, together with programmable logic cells. These devices
are known as Programmable System on Chip (PSoC). Their ARM
cores (embedded in the processing system or PS) communicates
with the programmable logic cells (PL) using ARM-standard AXI
buses. In this paper we analyses the performance of exhaustive
data transfers between PS and PL for a Xilinx Zynq FPGA
in a co-design real scenario for Convolutional Neural Networks
(CNN) accelerator, which processes, in dedicated hardware, a
stream of visual information from a neuromorphic visual sensor
for classification. In the PS side, a Linux operating system is
running, which recollects visual events from the neuromorphic
sensor into a normalized frame, and then it transfers these
frames to the accelerator of multi-layered CNNs, and read results,
using an AXI-DMA bus in a per-layer way. As these kind of
accelerators try to process information as quick as possible, data
bandwidth becomes critical and maintaining a good balanced
data throughput rate requires some considerations. We present
and evaluate several data partitioning techniques to improve the
balance between RX and TX transfer and two different ways
of transfers management: through a polling routine at the userlevel
of the OS, and through a dedicated interrupt-based kernellevel
driver. We demonstrate that for longer enough packets,
the kernel-level driver solution gets better timing in computing a
CNN classification example. Main advantage of using kernel-level
driver is to have safer solutions and to have tasks scheduling in
the OS to manage other important processes for our application,
like frames collection from sensors and their normalization.Ministerio de Economía y Competitividad TEC2016-77785-
FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations
Neural network-based methods for image processing are becoming widely used in
practical applications. Modern neural networks are computationally expensive
and require specialized hardware, such as graphics processing units. Since such
hardware is not always available in real life applications, there is a
compelling need for the design of neural networks for mobile devices. Mobile
neural networks typically have reduced number of parameters and require a
relatively small number of arithmetic operations. However, they usually still
are executed at the software level and use floating-point calculations. The use
of mobile networks without further optimization may not provide sufficient
performance when high processing speed is required, for example, in real-time
video processing (30 frames per second). In this study, we suggest
optimizations to speed up computations in order to efficiently use already
trained neural networks on a mobile device. Specifically, we propose an
approach for speeding up neural networks by moving computation from software to
hardware and by using fixed-point calculations instead of floating-point. We
propose a number of methods for neural network architecture design to improve
the performance with fixed-point calculations. We also show an example of how
existing datasets can be modified and adapted for the recognition task in hand.
Finally, we present the design and the implementation of a floating-point gate
array-based device to solve the practical problem of real-time handwritten
digit classification from mobile camera video feed
- …