16,562 research outputs found
FAST IMPLEMENTATION TECHNIQUES OF MULTICHANNEL DIGITAL FILTERS FOR COLOR IMAGE PROCESSING USING MATRIX DECOMPOSITIONS
For the processing of color images, multivariable 3-input, 3-output 2-D digital filters are
used, considering decomposition in the R, G and B components. Assuming that the three
image components are decorrelated, three independent single-input, single-output (SISO)
two-dimensional (2-D) digital filters are needed for the processing of each monochromatic
image. Additional processing is needed for the correlated noise components in each chan-
nel. The requirement of very fast processing dictates the use of special purpose hardware
implementations. The VLSI array processors, which are special purpose, locally intercon-
nected computing networks, are ideally suited for the fast implementation of digital filters,
since they maximize concurrency by exploiting both parallelism and pipelining. In this
paper fast implementation architectures of 3-input, 3-output 2-D multi-input digital filters
for color image processing that are based on matrix decompositions are presented. The
resulting structures are modular, regular, have high inherent parallelism and are easily
pipelined, so that they may be implemented via VLSI array processors
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Pipelined Two-Operand Modular Adders
Pipelined two-operand modular adder (TOMA) is one of basic components used in digital signal processing (DSP) systems that use the residue number system (RNS). Such modular adders are used in binary/residue and residue/binary converters, residue multipliers and scalers as well as within residue processing channels. The design of pipelined TOMAs is usually obtained by inserting an appriopriate number of latch layers inside a nonpipelined TOMA structure. Hence their area is also determined by the number of latches and the delay by the number of latch layers. In this paper we propose a new pipelined TOMA that is based on a new TOMA, that has the smaller area and smaller delay than other known structures. Comparisons are made using data from the very large scale of integration (VLSI) standard cell library
Design Of Neural Network Circuit Inside High Speed Camera Using Analog CMOS 0.35 ÂŒm Technology
Analog VLSI on-chip learning Neural Networks represent a mature technology for a large number of applications involving industrial as well as consumer appliances. This is particularly the case when low power consumption, small size and/or very high speed are required. This approach exploits the computational features of Neural Networks, the implementation efficiency of analog VLSI circuits and the adaptation capabilities of the on-chip learning feedback schema. High-speed video cameras are powerful tools for investigating for instance the biomechanics analysis or the movements of mechanical parts in manufacturing processes. In the past years, the use of CMOS sensors instead of CCDs has enabled the development of high-speed video cameras offering digital outputs , readout flexibility, and lower manufacturing costs. In this paper, we propose a high-speed smart camera based on a CMOS sensor with embedded Analog Neural Network
Concepts for on-board satellite image registration. Volume 3: Impact of VLSI/VHSIC on satellite on-board signal processing
Anticipated major advances in integrated circuit technology in the near future are described as well as their impact on satellite onboard signal processing systems. Dramatic improvements in chip density, speed, power consumption, and system reliability are expected from very large scale integration. Improvements are expected from very large scale integration enable more intelligence to be placed on remote sensing platforms in space, meeting the goals of NASA's information adaptive system concept, a major component of the NASA End-to-End Data System program. A forecast of VLSI technological advances is presented, including a description of the Defense Department's very high speed integrated circuit program, a seven-year research and development effort
High throughput spatial convolution filters on FPGAs
Digital signal processing (DSP) on field- programmable gate arrays (FPGAs) has long been appealing because of the inherent parallelism in these computations that can be easily exploited to accelerate such algorithms. FPGAs have evolved significantly to further enhance the mapping of these algorithms, included additional hard blocks, such as the DSP blocks found in modern FPGAs. Although these DSP blocks can offer more efficient mapping of DSP computations, they are primarily designed for 1-D filter structures. We present a study on spatial convolutional filter implementations on FPGAs, optimizing around the structure of the DSP blocks to offer high throughput while maintaining the coefficient flexibility that other published architectures usually sacrifice. We show that it is possible to implement large filters for large 4K resolution image frames at frame rates of 30â60 FPS, while maintaining functional flexibility
Individual flip-flops with gated clocks for low power datapaths
Energy consumption has become one of the important factors in digital systems, because of the requirement to dissipate this energy in high-density circuits and to extend the battery life in portable systems such as devices with wireless communication capabilities. Flip-flops are one of the most energy-consuming components of digital circuits. This paper presents techniques to reduce energy consumption by individually deactivating the clock when flip-flops do not have to change their value. Flip-flop structures are proposed and selection criteria given to obtain minimum energy consumption. The structures have been evaluated using energy models and validated by switch-level simulations. For the applications considered, significant energy reductions are achieved.Peer ReviewedPostprint (published version
Architectures for block Toeplitz systems
In this paper efficient VLSI architectures of highly concurrent algorithms for the solution of block linear systems with Toeplitz or near-to-Toeplitz entries are presented. The main features of the proposed scheme are the use of scalar only operations, multiplications/divisions and additions, and the local communication which enables the development of wavefront array architecture. Both the mean squared error and the total squared error formulations are described and a variety of implementations are given
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