Residue Arithmetic VLSI Array Architecture for Manipulator Pseudo-Inverse Jacobian Computation

Most Cartesian-based control strategies require the computation of the manipulator inverse Jacobian in real time at every sampling period. In some cases, the Jacobian matrix is not of full column or row rank due to singularity or redundant robot configuration. This requires the computation of the manipulator pseudo-inverse Jacobian in real time. The calculation of the pseudo-inverse Jacobian may become extremely sensitive to small perturbation in the data and numerical instabilities, when the Jacobian matrix is not of full column or row rank. Even if the Jacobian matrix is of full rank, the ill-conditioned problem may still plague the computation of the pseudoinverse Jacobian. This paper presents the use of residue arithmetic for the exact computation of the manipulator pseudo-inverse Jacobian to obviate the roundoff errors normally associated with the computations. A two-level macro-pipelined residue arithmetic array architecture implementing the Decell’s pseudo-inverse algorithm has been developed to overcome the ill-conditioned problem of the pseudo-inverse computation. Furthermore, the Decell algorithm is quite suitable for VLSI array implementation to achieve the real-time computation requirement. The first-level arrays are data-driven, wavefront-like arrays and perform the matrix multiplications, matrix diagonal additions, and trace computations. A pool or sequence of the first-level arrays are then configured into a second-level macro-pipeline with outputs of one array acting as inputs to another array in the pipe. The proposed architecture can calculate the pseudoinverse Jacobian with a pipelined time in the same computational complexity order as evaluating a matrix product in a wavefront array

The Unification and Decomposition of Processing Structures Using Lattice Theoretic Methods

The purpose of this dissertation is to demonstrate that lattice theoretic methods can be used to decompose and unify computational structures over a variety of processing systems. The unification arguments provide a better understanding of the intricacies of the development of processing system decomposition. Since abstract algebraic techniques are used, the decomposition process is systematized which makes it conducive to the use of computers as tools for decomposition. A general algorithm using the lattice theoretic method is developed to examine the structures and therefore decomposition properties of integer and polynomial rings. Two fundamental representations, the Sino-correspondence and the weighted radix representation, are derived for integer and polynomial structures and are shown to be a natural result of the decomposition process. They are used in developing systematic methods for decomposing discrete Fourier transforms and discrete linear systems. That is, fast Fourier transforms and partial fraction expansions of linear systems are a result of the natural representation derived using the lattice theoretic method. The discrete Fourier transform is derived from a lattice theoretic base demonstrating its independence of the continuous form and of the field over which it is computed. The same properties are demonstrated for error control codes based on polynomials. Partial fraction expansions are shown to be independent of the concept of a derivative for repeated roots and the field used to implement them

High speed convolution using residue number systems

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.Title as it appears in the M.I.T. Graduate List, Feb. 1989: Number theoretic methods in digital signal processing.Includes bibliographical references (leaves 124-126).by Kurt Anthony Locher.M.S

A computer-aided design for digital filter implementation

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What grid cells convey about rat location

We characterize the relationship between the simultaneously recorded quantities of rodent grid cell firing and the position of the rat. The formalization reveals various properties of grid cell activity when considered as a neural code for representing and updating estimates of the rat's location. We show that, although the spatially periodic response of grid cells appears wasteful, the code is fully combinatorial in capacity. The resulting range for unambiguous position representation is vastly greater than the ≈1–10 m periods of individual lattices, allowing for unique high-resolution position specification over the behavioral foraging ranges of rats, with excess capacity that could be used for error correction. Next, we show that the merits of the grid cell code for position representation extend well beyond capacity and include arithmetic properties that facilitate position updating. We conclude by considering the numerous implications, for downstream readouts and experimental tests, of the properties of the grid cell code

Integrated photonics modular arithmetic processor

Integrated photonics computing has emerged as a promising approach to overcome the limitations of electronic processors in the post-Moore era, capitalizing on the superiority of photonic systems. However, present integrated photonics computing systems face challenges in achieving high-precision calculations, consequently limiting their potential applications, and their heavy reliance on analog-to-digital (AD) and digital-to-analog (DA) conversion interfaces undermines their performance. Here we propose an innovative photonic computing architecture featuring scalable calculation precision and a novel photonic conversion interface. By leveraging Residue Number System (RNS) theory, the high-precision calculation is decomposed into multiple low-precision modular arithmetic operations executed through optical phase manipulation. Those operations directly interact with the digital system via our proposed optical digital-to-phase converter (ODPC) and phase-to-digital converter (OPDC). Through experimental demonstrations, we showcase a calculation precision of 9 bits and verify the feasibility of the ODPC/OPDC photonic interface. This approach paves the path towards liberating photonic computing from the constraints imposed by limited precision and AD/DA converters.Comment: 23 pages, 9 figure