On prefilters for digital FIR filter design

A new family of digital prefilter structures is introduced, based on the Dolph-Chebyshev function. These prefilters can be combined with appropriately designed "equalizer" filters based on equiripple methods, leading to efficient FIR digital filter designs. Design examples are included, demonstrating the simplicity of the resulting designs, as compared to conventional equiripple designs

Latency Optimized Asynchronous Early Output Ripple Carry Adder based on Delay-Insensitive Dual-Rail Data Encoding

Asynchronous circuits employing delay-insensitive codes for data representation i.e. encoding and following a 4-phase return-to-zero protocol for handshaking are generally robust. Depending upon whether a single delay-insensitive code or multiple delay-insensitive code(s) are used for data encoding, the encoding scheme is called homogeneous or heterogeneous delay-insensitive data encoding. This article proposes a new latency optimized early output asynchronous ripple carry adder (RCA) that utilizes single-bit asynchronous full adders (SAFAs) and dual-bit asynchronous full adders (DAFAs) which incorporate redundant logic and are based on the delay-insensitive dual-rail code i.e. homogeneous data encoding, and follow a 4-phase return-to-zero handshaking. Amongst various RCA, carry lookahead adder (CLA), and carry select adder (CSLA) designs, which are based on homogeneous or heterogeneous delay-insensitive data encodings which correspond to the weak-indication or the early output timing model, the proposed early output asynchronous RCA that incorporates SAFAs and DAFAs with redundant logic is found to result in reduced latency for a dual-operand addition operation. In particular, for a 32-bit asynchronous RCA, utilizing 15 stages of DAFAs and 2 stages of SAFAs leads to reduced latency. The theoretical worst-case latencies of the different asynchronous adders were calculated by taking into account the typical gate delays of a 32/28nm CMOS digital cell library, and a comparison is made with their practical worst-case latencies estimated. The theoretical and practical worst-case latencies show a close correlation....Comment: arXiv admin note: text overlap with arXiv:1704.0761

Quantum autoencoders via quantum adders with genetic algorithms

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms

Loop pipelining with resource and timing constraints

Developing efficient programs for many of the current parallel computers is not easy due to the architectural complexity of those machines. The wide variety of machine organizations often makes it more difficult to port an existing program than to reprogram it completely. Therefore, powerful translators are necessary to generate effective code and free the programmer from concerns about the specific characteristics of the target machine. This work focuses on techniques to be used by an important class of translators, whose objective is to transform sequential programs into equivalent more parallel programs. The transformations are performed at instruction level in order to exploit low level parallelism and increase memory locality.Most of the current applications are programmed in languages which do not allow us to express parallelism between high-level sentences (as Pascal, C or Fortran). Furthermore, a lot of applications written ten or more years ago are still used today, and it is not feasible to rewrite such applications for many reasons (not only technical reasons, but also economic ones). Translators enable programmers to write the application in a familiar sequential programming language, without concerning their selves with the architecture of the target machine. Current compilers for parallel architectures not only translate a program written on a high-level language to the appropriate machine language, but also perform some transformations in the final code in order to execute the program in a more parallel way. The transformations improve the performance in the execution of the program by making use of the knowledge that the compiler has about the machine architecture. The semantics of the program remain intact after any transformation.Experiments show that limiting parallelization to basic blocks not included in loops limits maximum speedup. This is because loops often comprise a large portion of the parallelism available to be exploited in a program. For this reason, a lot of effort has been devoted in the recent years to parallelize loop execution. Several parallel computer architectures and compilation techniques have been proposed to exploit such a parallelism at different granularities. Multiprocessors exploit coarse grained parallelism by distributing entire loop iterations to different processors. Systems oriented to the high-level synthesis (HLS) of VLSI circuits, superscalar processors and very long instruction word (VLIW) processors exploit fine-grained parallelism at instruction level. This work addresses fine-grained parallelization of loops addressed to the HLS of VLSI circuits. Two algorithms are proposed for resource constraints and for timing constraints. An algorithm to reduce the number of registers required to execute a loop in a given architecture is also proposed.Postprint (published version

Improved Memoryless RNS Forward Converter Based on the Periodicity of Residues

The residue number system (RNS) is suitable for DSP architectures because of its ability to perform fast carry-free arithmetic. However, this advantage is over-shadowed by the complexity involved in the conversion of numbers between binary and RNS representations. Although the reverse conversion (RNS to binary) is more complex, the forward transformation is not simple either. Most forward converters make use of look-up tables (memory). Recently, a memoryless forward converter architecture for arbitrary moduli sets was proposed by Premkumar in 2002. In this paper, we present an extension to that architecture which results in 44% less hardware for parallel conversion and achieves 43% improvement in speed for serial conversions. It makes use of the periodicity properties of residues obtained using modular exponentiation