Throughput constrained parallelism reduction in cyclo-static dataflow applications

International audienceThis paper deals with semantics-preserving parallelism reduction methods for cyclo-static dataflow applications. Parallelism reduction is the process of equivalent actors fusioning. The principal objectives of parallelism reduction are to decrease the memory footprint of an application and to increase its execution performance. We focus on parallelism reduction methodologies constrained by application throughput. A generic parallelism reduction methodology is introduced. Experimental results are provided for asserting the performance of the proposed method

A compression method for homomorphic ciphertexts

In this work we describe a message packing and unpacking method for homomorphic ciphertexts. Messages are packed into the coefficients of plaintext polynomials. We propose an unpacking procedure which allows to obtain a ciphertext for each packed message. The packing and unpacking of ciphertexts represents a solution for reducing the transmission bottleneck in cloud based applications, in particular when sending homomorphic calculations results. The results we obtain (packing ratio, unpacking time) are compared to existing packing methods based on trans-ciphering

Meta-learning framework with applications to zero-shot time-series forecasting

Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models

Профессор И. Г. Фрейман - выдающийся советский радиотехник

Article is prepared for printing L. I. Zolotinkina, is printed with some reductions concerning generally of the mathematical calculations confirming theoretical results of scientific researches I. G. Freymana. Reflection still of very important direction of works of I. G. Freyman connected with use of electrovacuum devices. In a number of its articles for the first time in domestic technical literature questions of terminology and determination of the main sizes characterizing operation of "hollow devices" were considered, methods of an assessment of quality of electron tubes are theoretically proved and offered. More than 60 publications and books are included into the list of scientific works of professor Freyman, practically all of them were the first scientific works in a number of the new scientific directions created as a result of development of radio engineering, considering the first publication - article A. S. Popova (1895).1 мая 2015 г. исполнилось 125 лет со дня рождения профессора Иманта Георгиевича Фреймана (1890-1929). В архиве Музея истории СПбГЭТУ "ЛЭТИ" в папке "Личное дело проф. И. Г. Фреймана" среди других документов хранится копия статьи ученика И. Г. Фреймана выпускника ЛЭТИ 1927 г., кандидата технических наук, доцента Академии им. А. Ф. Можайского В. Г. Карпова, опубликованная в 1949 г. в "Трудах Академии" к 20-летию со дня кончины ученого. К началу XXI в. радиоэлектроника "умчалась" далеко вперед, нам сейчас трудно оценить значение отдельных научных работ, выполненных в первые десятилетия ХХ в. Поэтому представляет особый интерес мнение специалиста, работавшего в те, уже далекие годы, в которые непосредственно видны были результаты научной, педагогической и практической деятельности И. Г. Фреймана. Ученику удалось передать искреннее отношение огромного уважения и признательности своему Учителю

Stream ciphers: A Practical Solution for Efficient Homomorphic-Ciphertext Compression

International audienceIn typical applications of homomorphic encryption, the first step consists for Alice to encrypt some plaintext m under Bob’s public key pk and to send the ciphertext c = HEpk(m) to some third-party evaluator Charlie. This paper specifically considers that first step, i.e. the problem of transmitting c as efficiently as possible from Alice to Charlie. As previously noted, a form of compression is achieved using hybrid encryption. Given a symmetric encryption scheme E, Alice picks a random key k and sends a much smaller ciphertext c′ = (HEpk(k), Ek(m)) that Charlie decompresses homomorphically into the original c using a decryption circuit CE−1 .In this paper, we revisit that paradigm in light of its concrete implemen- tation constraints; in particular E is chosen to be an additive IV-based stream cipher. We investigate the performances offered in this context by Trivium, which belongs to the eSTREAM portfolio, and we also pro- pose a variant with 128-bit security: Kreyvium. We show that Trivium, whose security has been firmly established for over a decade, and the new variant Kreyvium have an excellent performance

Towards real-time hidden speaker recognition by means of fully homomorphic encryption

Securing Neural Network (NN) computations through the use of Fully Homomorphic Encryption (FHE) is the subject of a growing interest in both communities. Among different possible approaches to that topic, our work focuses on applying FHE to hide the model of a neural network-based system in the case of a plain input. In this paper, using the TFHE homomorphic encryption scheme, we propose an efficient fully homomorphic method for an argmin computation on an arbitrary number of encrypted inputs and an asymptotically faster - though levelled - equivalent scheme. Using these schemes and a unifying framework for LWE-based homomorphic encryption schemes (Chimera), we implement a very time-wise efficient, homomorphic speaker recognition scheme using the neural-based embedding system VGGVox. This work can be generalized to all other similar Euclidean embedding-based recognition systems. While maintaining the best-of-class classification rate of the VGGVox system, we implement a speaker-recognition system that can classify a speech sample as coming from one of a 100 hidden model speakers in less than one second

Faster homomorphic encryption is not enough: improved heuristic for multiplicative depth minimization of Boolean circuits

In somewhat homomorphic encryption schemes (e.g. B/FV, BGV) the size of ciphertexts and the execution performance of homomorphic operations depends heavily on the multiplicative depth. The multiplicative depth is the maximal number of consecutive multiplications for which an homomorphic encryption scheme was parameterized. In this work we propose an improved multiplicative depth minimization heuristic. In particular, a new circuit rewriting operator is introduced, the so called cone rewrite operator. The results we obtain using the new method are relevant in terms of accuracy and performance. Smaller multiplicative depths for a benchmark of Boolean circuits are obtained when compared to a previous work found in the literature. In average, the multiplicative depth is highly improved and the new heuristic execution time is significantly lower. The proposed rewrite operator and heuristic are not limited to Boolean circuits, but can also be used for arithmetic circuits