Abstract
VLSI applications include Digital Signal Processing, Digital control systems, Telecommunications, Speech and Audio processing for audiology and speech language pathology. The latest research in VLSI is the design and implementation of DSP systems which are essential for above applications. The fundamental computation in DSP Systems is convolution. Convolution and LTI systems are the heart and soul of DSP. The behavior of LTI systems in continuous time is described by Convolution integral whereas the behavior in discrete-time is described by Linear convolution. In this paper, Linear convolution is performed using carry save multiplier architecture based on vertical and cross wise algorithm of Urdhva – Tiryagbhyam in Vedic mathematics. Coding is done using Verilog HDL(verilog Hardware Description Language). Simulation and Synthesis are performed using Xilinx FPGA
Highlights
In this paper, carry save multiplier architecture is developed using Urdhva-Tiryagbhyam sutra
The output sequence y[n] of a linear time invariant system, with impulse response h[n] due to any input sequence x[n] is the convolution sum of x[n] with h[n] and is given as Linear convolution which is a fundamental computation in Linear time-invariant (LTI) systems is implemented using Verilog HDL
The overall speed in multiplication depends on number of partial products generated, shifting the partial products based on bit position and summation of partial products
Summary
Carry save multiplier architecture is developed using Urdhva-Tiryagbhyam sutra. This sutra is applied to perform multiplication of size NXN. The output sequence y[n] of a linear time invariant system, with impulse response h[n] due to any input sequence x[n] is the convolution sum of x[n] with h[n] and is given as Linear convolution which is a fundamental computation in Linear time-invariant (LTI) systems is implemented using Verilog HDL. The carry bits are passed diagonally downwards, which requires a vector merging adder to obtain final sum of all the partial products[1]. Fundamental computations includes multiplication and addition of input and impulse signals or samples[2]
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