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New type of m-ary systolic arrays called reversible systolic arrays is introduced in this paper. The m-ary quantum systolic architectures' realizations and computations of the new type of systolic arrays are also introduced. A systolic array is an example of a single-instruction multiple-data (SIMD) machine in which each processing element (PE) performs a single simple operation. Systolic devices provide inexpensive but massive computation power, and are cost-effective, high-performance, and special-purpose systems that have wide range of applications such as in solving several regular and computebound problems containing repetitive multiple operations on large arrays of data. Similar to the classical case, information in a reversible and quantum systolic circuit flows between cells in a pipelined fashion, and communication with the outside world occurs only at the boundary cells. Since basic PEs used in the construction of arithmetic systolic arrays are the add-multiply cells, the results introduced in this paper are general and apply to a very wide range of add-multiply-based systolic arrays. Since the reduction of power consumption is a major requirement for the circuit design in future technologies, such as in quantum computing, the main features of several future technologies will include reversibility. Consequently, the new systolic circuits can play an important task in the design of future circuits that consume minimal power. It is also shown that the new systolic arrays maintain the high level of regularity while exhibiting the new fundamental bijectivity (reversibility) and quantum superposition properties. These new properties will be essential in performing super-fast arithmetic-intensive computations that are fundamental in several future applications such as in multi-dimensional quantum signal processing (QSP). IntroductionDue to the anticipated failure of Moore's law around the year 2020, quantum computing will hopefully play an increasingly crucial role in building more compact and less power consuming computers. 1,7,23,30,65,66,82 Due to this fact, and because all quantum computer gates (i.e., fundamental building blocks) should be reversible, [1][2][3]7,9,18,23,30,57,65,66,72,82 reversible computing will have an increasingly more existence in the future design of regular, compact, and universal circuits. (k, k) reversible circuits are circuits that have the same number of inputs k and outputs k and are one-to-one mappings between vectors of inputs and outputs, thus the vector of input states can be always uniquely reconstructed from the vector of output states. 1,9,18,23,57,66 As was proven in Ref. 57, it is a necessary (but not sufficient) condition for not dissipating power in any physical circuit that all system circuits must be built using fully reversible logical components. For this reason, different technologies have been studied to implement reversible logic in hardware like adiabatic CMOS, 78 optical, 72 and quantum. 1,66 Fully reversible digital systems will greatly reduce the power co...
New type of m-ary systolic arrays called reversible systolic arrays is introduced in this paper. The m-ary quantum systolic architectures' realizations and computations of the new type of systolic arrays are also introduced. A systolic array is an example of a single-instruction multiple-data (SIMD) machine in which each processing element (PE) performs a single simple operation. Systolic devices provide inexpensive but massive computation power, and are cost-effective, high-performance, and special-purpose systems that have wide range of applications such as in solving several regular and computebound problems containing repetitive multiple operations on large arrays of data. Similar to the classical case, information in a reversible and quantum systolic circuit flows between cells in a pipelined fashion, and communication with the outside world occurs only at the boundary cells. Since basic PEs used in the construction of arithmetic systolic arrays are the add-multiply cells, the results introduced in this paper are general and apply to a very wide range of add-multiply-based systolic arrays. Since the reduction of power consumption is a major requirement for the circuit design in future technologies, such as in quantum computing, the main features of several future technologies will include reversibility. Consequently, the new systolic circuits can play an important task in the design of future circuits that consume minimal power. It is also shown that the new systolic arrays maintain the high level of regularity while exhibiting the new fundamental bijectivity (reversibility) and quantum superposition properties. These new properties will be essential in performing super-fast arithmetic-intensive computations that are fundamental in several future applications such as in multi-dimensional quantum signal processing (QSP). IntroductionDue to the anticipated failure of Moore's law around the year 2020, quantum computing will hopefully play an increasingly crucial role in building more compact and less power consuming computers. 1,7,23,30,65,66,82 Due to this fact, and because all quantum computer gates (i.e., fundamental building blocks) should be reversible, [1][2][3]7,9,18,23,30,57,65,66,72,82 reversible computing will have an increasingly more existence in the future design of regular, compact, and universal circuits. (k, k) reversible circuits are circuits that have the same number of inputs k and outputs k and are one-to-one mappings between vectors of inputs and outputs, thus the vector of input states can be always uniquely reconstructed from the vector of output states. 1,9,18,23,57,66 As was proven in Ref. 57, it is a necessary (but not sufficient) condition for not dissipating power in any physical circuit that all system circuits must be built using fully reversible logical components. For this reason, different technologies have been studied to implement reversible logic in hardware like adiabatic CMOS, 78 optical, 72 and quantum. 1,66 Fully reversible digital systems will greatly reduce the power co...
hen Sun Microsystems introduced its first workstation, the company could not have imagined how quickly workstations would revolutionize computing. The idea of a community of engineers, scientists, or researchers time-sharing on a single mainframe computer could hardly have become ancient any more quickly. The almost instant wide acceptance of workstations and desktop computers indicates that [hey were quickly recognized as giving the best and most flexible performance for the dollar.Desktop computers proliferated for three reasons. First, very large scale integration (VLSI) and wafer scale integration (WSI), despite some problems, increased the gate density of chips while dramatically lowering their production cost.' Moreover. increased gate density permits a more complicated processor, which in turn promotes parallelism.Second, desktop computers distribute processing power to the user in an easily customized open architecture. Rcal-time applications that require intensive 110 and computation need not consume all the resources of a supercomputer. Also, desktop computers support high-definition screens with color and motion far exceeding those available with any multiple-user. shared-resource mainframe.Third, economical. high-bandwidth networks allow desktop computers to share data. thus retaining the most appealing aspect of centralized computing, resource sharing. Moreover, networks allow the computers to share data with dissimilar computing machines. That is perhaps the most important reason for the acceptance of desktop computers. since all the performance in the world is worth little if the machine is isolated.Today's workstations have redefined the way the computing community distributes processing resources, and tomorrow's machines will continue this trend with higher bandwidth networks and higher computational performance. One way to obtain higher computational performance is t o use special parallel coprocessors t o perform functions such as motion and color support of high-definition screens. Future computationally intensive applications suited for desktop computing machines include real-time text, speech, and image processing. These applications require massive parallelism.'
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