The Continuous Valued Number System (CVNS) is a novel analog digit number system which employs bit level analog residue arithmetic. The information redundancy among the digits, makes it easy to perform the required binary operations in higher radices, and reduces the implementation area and the number of required interconnections. CVNS theory can open up a new approach for performing digital arithmetic with simple and elementary analog elements, such as current comparators and current mirrors, and with arbitrary precision. In this paper we discuss the design of 16-bit radix-4 CVNS adder with controlled precision, and a two operand binary adder designed, in TSMC CMOS 0.18μm technology, is used to illustrate the techniques.
The conventional trend in algorithm implementation has been the reliance on advancements in process technology in order to satisfy the ever-increasing demand for high-speed processors, and computational systems. As current device technology approaches sub-100nm minimum device size, not only does the device geometry decrease, but switching times, and operating voltages also scale down. These gains come at the expense of increased layout complexity, and a greater susceptibility to parasitic effects in the interconnections. In this paper we will briefly overview the challenges that digital designers will have to face in the imminent future, and will provide suggestions on algorithmic measures which may be taken in order to overcome some of these obstacles. To
illustrate our point, we will present an analysis of a digital multiplication algorithm, which is predicted to outperform current
schemes, for future technologies.
The design of two microelectromechanical (MEMS) devices that form pat of a micro acousto-magnetic transducer for use with a hearing-aid instrument is described in this paper. The transducer will convert acoustical energy into an electrical signal using a MEMS realization of a capacitive microphone. The output signal from the microphone undergoes signal conditioning and processing in order to drive a MEMS electromagnetic actuator. The resultant magnetic fid is used to exert a force on a high coercivity permanent micro magnet that has been implanted on the round window of the cochlea. The motion of the implanted magnet will develop traveling waves on the basilar membrane inside the cochlea to give a hearing capability. A high-sensitivity MEMS based capacitor microphone is designed using a polysilicon Germanium diaphragm. The microphone is constructed using a combination of surface and bulk micro machining techniques, in a single wafer process. The microphone diaphragm has a proposed thickness of 0.7 micrometers , an area of 2.6 mm2, an air gap of 3.0 micrometers and a 1 micrometers thick silicon nitride backplate with acoustical ports. An output voltage signal is obtained from the capacitor microphone using a capacitive voltage divider network and amplified by a simple source follower circuit. D
In this paper, we propose a training algorithm for VLSI neural networks with digital weights and analog neurons using in-the-loop training strategy. The use of digital weights in a neural network implementation imposes new issues that are not present in simulation environments. One of the problems is that a neural network implementation will not work properly when using the digitized version of the continuous weight solution. This phenomenon is especially evident when the digital weight resolution is very low due to some fabrication constraints. In this paper the training strategies for dealing with digital weights are investigated. The proposed training algorithm is by measuring the sensitivity of each weight to its error function and then by perturbing the weights of higher sensitivity values to perform retraining process. Our experimental results indicate that the algorithm is feasible and particularly suitable for the digital weights with low number of bits.
In this paper a new approach to image recognition using feature extraction based on a revised nearest neighbor clustering method is described. A set of candidate feature vectors are formed by using the Gabor transform of the sample image to compute a number of Gabor kernels with different frequency and orientation parameters. Each of the candidate feature vectors is then sequentially inputted to a self- organizing neural network architecture that is used in conjunction with a revised nearest-neighbor algorithm. The revised nearest-neighbor method assigns an input vector to the nearest prototype (code book vector) when the distance between them is found to be within a preset threshold, and creates a new prototype when the distance is larger than the preset threshold value. The distance computation is conducted by measuring the saliency among the vectors of interest, which differs from traditional norms (e.g. Euclidean norm). Simulation results show that the proposed method is efficient in extracting feature vectors from images. These feature vectors are representative of the image and can be applied to image identification. The novelty associated with this work lies in the use of the saliency of feature vectors as the distance norm and a growing cell self-organizing structure to capture the feature vectors.
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