2,301 research outputs found
Word-length optimization of folded polynomial evaluation
Published versio
PaReNTT: Low-Latency Parallel Residue Number System and NTT-Based Long Polynomial Modular Multiplication for Homomorphic Encryption
High-speed long polynomial multiplication is important for applications in
homomorphic encryption (HE) and lattice-based cryptosystems. This paper
addresses low-latency hardware architectures for long polynomial modular
multiplication using the number-theoretic transform (NTT) and inverse NTT
(iNTT). Chinese remainder theorem (CRT) is used to decompose the modulus into
multiple smaller moduli. Our proposed architecture, namely PaReNTT, makes four
novel contributions. First, parallel NTT and iNTT architectures are proposed to
reduce the number of clock cycles to process the polynomials. This can enable
real-time processing for HE applications, as the number of clock cycles to
process the polynomial is inversely proportional to the level of parallelism.
Second, the proposed architecture eliminates the need for permuting the NTT
outputs before their product is input to the iNTT. This reduces latency by n/4
clock cycles, where n is the length of the polynomial, and reduces buffer
requirement by one delay-switch-delay circuit of size n. Third, an approach to
select special moduli is presented where the moduli can be expressed in terms
of a few signed power-of-two terms. Fourth, novel architectures for
pre-processing for computing residual polynomials using the CRT and
post-processing for combining the residual polynomials are proposed. These
architectures significantly reduce the area consumption of the pre-processing
and post-processing steps. The proposed long modular polynomial multiplications
are ideal for applications that require low latency and high sample rate as
these feed-forward architectures can be pipelined at arbitrary levels
Optimized Entanglement Purification
We investigate novel protocols for entanglement purification of qubit Bell
pairs. Employing genetic algorithms for the design of the purification circuit,
we obtain shorter circuits achieving higher success rates and better final
fidelities than what is currently available in the literature. We provide a
software tool for analytical and numerical study of the generated purification
circuits, under customizable error models. These new purification protocols
pave the way to practical implementations of modular quantum computers and
quantum repeaters. Our approach is particularly attentive to the effects of
finite resources and imperfect local operations - phenomena neglected in the
usual asymptotic approach to the problem. The choice of the building blocks
permitted in the construction of the circuits is based on a thorough
enumeration of the local Clifford operations that act as permutations on the
basis of Bell states
Distance-regular graphs
This is a survey of distance-regular graphs. We present an introduction to
distance-regular graphs for the reader who is unfamiliar with the subject, and
then give an overview of some developments in the area of distance-regular
graphs since the monograph 'BCN' [Brouwer, A.E., Cohen, A.M., Neumaier, A.,
Distance-Regular Graphs, Springer-Verlag, Berlin, 1989] was written.Comment: 156 page
Ideal-Theoretic Explanation of Capacity-Achieving Decoding
In this work, we present an abstract framework for some algebraic error-correcting codes with the aim of capturing codes that are list-decodable to capacity, along with their decoding algorithm. In the polynomial ideal framework, a code is specified by some ideals in a polynomial ring, messages are polynomials and their encoding is the residue modulo the ideals. We present an alternate way of viewing this class of codes in terms of linear operators, and show that this alternate view makes their algorithmic list-decodability amenable to analysis.
Our framework leads to a new class of codes that we call affine Folded Reed-Solomon codes (which are themselves a special case of the broader class we explore). These codes are common generalizations of the well-studied Folded Reed-Solomon codes and Univariate Multiplicity codes, while also capturing the less-studied Additive Folded Reed-Solomon codes as well as a large family of codes that were not previously known/studied.
More significantly our framework also captures the algorithmic list-decodability of the constituent codes. Specifically, we present a unified view of the decoding algorithm for ideal-theoretic codes and show that the decodability reduces to the analysis of the distance of some related codes. We show that good bounds on this distance lead to capacity-achieving performance of the underlying code, providing a unifying explanation of known capacity-achieving results. In the specific case of affine Folded Reed-Solomon codes, our framework shows that they are list-decodable up to capacity (for appropriate setting of the parameters), thereby unifying the previous results for Folded Reed-Solomon, Multiplicity and Additive Folded Reed-Solomon codes
Programming models, compilers, and runtime systems for accelerator computing
Accelerators, such as GPUs and Intel Xeon Phis, have become the workhorses of high-performance computing. Typically, the accelerators act as co-processors, with discrete memory spaces. They possess massive parallelism, along with many other unique architectural features. In order to obtain high performance, these features must be carefully exploited, which requires high programmer expertise. This thesis presents new programming models, and the necessary compiler and runtime systems to ease the accelerator programming process, while obtaining high performance
Randomly punctured Reed--Solomon codes achieve list-decoding capacity over linear-sized fields
Reed--Solomon codes are a classic family of error-correcting codes consisting
of evaluations of low-degree polynomials over a finite field on some sequence
of distinct field elements. They are widely known for their optimal
unique-decoding capabilities, but their list-decoding capabilities are not
fully understood. Given the prevalence of Reed-Solomon codes, a fundamental
question in coding theory is determining if Reed--Solomon codes can optimally
achieve list-decoding capacity.
A recent breakthrough by Brakensiek, Gopi, and Makam, established that
Reed--Solomon codes are combinatorially list-decodable all the way to capacity.
However, their results hold for randomly-punctured Reed--Solomon codes over an
exponentially large field size , where is the block length of the
code. A natural question is whether Reed--Solomon codes can still achieve
capacity over smaller fields. Recently, Guo and Zhang showed that Reed--Solomon
codes are list-decodable to capacity with field size . We show that
Reed--Solomon codes are list-decodable to capacity with linear field size
, which is optimal up to the constant factor. We also give evidence that
the ratio between the alphabet size and code length cannot be bounded
by an absolute constant.
Our proof is based on the proof of Guo and Zhang, and additionally exploits
symmetries of reduced intersection matrices. With our proof, which maintains a
hypergraph perspective of the list-decoding problem, we include an alternate
presentation of ideas of Brakensiek, Gopi, and Makam that more directly
connects the list-decoding problem to the GM-MDS theorem via a hypergraph
orientation theorem
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