scholarly journals SIFO: Secure Computational Infrastructure Using FPGA Overlays

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Xin Fang ◽  
Stratis Ioannidis ◽  
Miriam Leeser
Keyword(s):  
Code Generation ◽  
Heterogeneous Computing ◽  
Computational Cost ◽  
Personal Data ◽  
Secure Computation ◽  
Coarse Grained ◽  
Function Evaluation ◽  
Fpga Design ◽  
Computing Platform ◽  
Garbled Circuits

Secure Function Evaluation (SFE) has received recent attention due to the massive collection and mining of personal data, but remains impractical due to its large computational cost. Garbled Circuits (GC) is a protocol for implementing SFE which can evaluate any function that can be expressed as a Boolean circuit and obtain the result while keeping each party’s input private. Recent advances have led to a surge of garbled circuit implementations in software for a variety of different tasks. However, these implementations are inefficient, and therefore GC is not widely used, especially for large problems. This research investigates, implements, and evaluates secure computation generation using a heterogeneous computing platform featuring FPGAs. We have designed and implemented SIFO: secure computational infrastructure using FPGA overlays. Unlike traditional FPGA design, a coarse-grained overlay architecture is adopted which supports mapping SFE problems that are too large to map to a single FPGA. Host tools provided include SFE problem generator, parser, and automatic host code generation. Our design allows repurposing an FPGA to evaluate different SFE tasks without the need for reprogramming and fully explores the parallelism for any GC problem. Our system demonstrates an order of magnitude speedup compared with an existing software platform.

scholarly journals Gage MPC: Bypassing Residual Function Leakage for Non-Interactive MPC

2021 ◽  
Vol 2021 (4) ◽  
pp. 528-548
Author(s):  
Ghada Almashaqbeh ◽  
Fabrice Benhamouda ◽  
Seungwook Han ◽  
Daniel Jaroslawicz ◽  
Tal Malkin ◽  
...  
Keyword(s):  
Computational Cost ◽  
Secure Computation ◽  
Residual Function ◽  
A Value ◽  
Sealed Bid ◽  
Garbled Circuits ◽  
Garbled Circuit ◽  
Time Capsule ◽  
Security Guarantees ◽  
Computational Resources

Abstract Existing models for non-interactive MPC cannot provide full privacy for inputs, because they inherently leak the residual function (i.e., the output of the function on the honest parties’ input together with all possible values of the adversarial inputs). For example, in any non-interactive sealed-bid auction, the last bidder can figure out what was the highest previous bid. We present a new MPC model which avoids this privacy leak. To achieve this, we utilize a blockchain in a novel way, incorporating smart contracts and arbitrary parties that can be incentivized to perform computation (“bounty hunters,” akin to miners). Security is maintained under a monetary assumption about the parties: an honest party can temporarily supply a recoverable collateral of value higher than the computational cost an adversary can expend. We thus construct non-interactive MPC protocols with strong security guarantees (full security, no residual leakage) in the short term. Over time, as the adversary can invest more and more computational resources, the security guarantee decays. Thus, our model, which we call Gage MPC, is suitable for secure computation with limited-time secrecy, such as auctions. A key ingredient in our protocols is a primitive we call “Gage Time Capsules” (GaTC): a time capsule that allows a party to commit to a value that others are able to reveal but only at a designated computational cost. A GaTC allows a party to commit to a value together with a monetary collateral. If the original party properly opens the GaTC, it can recover the collateral. Otherwise, the collateral is used to incentivize bounty hunters to open the GaTC. This primitive is used to ensure completion of Gage MPC protocols on the desired inputs. As a requisite tool (of independent interest), we present a generalization of garbled circuit that are more robust: they can tolerate exposure of extra input labels. This is in contrast to Yao’s garbled circuits, whose secrecy breaks down if even a single extra label is exposed. Finally, we present a proof-of-concept implementation of a special case of our construction, yielding an auction functionality over an Ethereum-like blockchain.

Parameterization of Divalent Cations for Coarse-Grained Simulations

2020 ◽  
Author(s):  
Florencia Klein ◽  
Daniela Cáceres-Rojas ◽  
Monica Carrasco ◽  
Juan Carlos Tapia ◽  
Julio Caballero ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Metal Ions ◽  
Molecular Dynamics Simulations ◽  
Divalent Cations ◽  
Computational Cost ◽  
Data Bank ◽  
Coarse Grained ◽  
Interaction Parameters ◽  
Dynamics Simulations ◽  
Dynamical Description

<p>Although molecular dynamics simulations allow for the study of interactions among virtually all biomolecular entities, metal ions still pose significant challenges to achieve an accurate structural and dynamical description of many biological assemblies. This is particularly the case for coarse-grained (CG) models. Although the reduced computational cost of CG methods often makes them the technique of choice for the study of large biomolecular systems, the parameterization of metal ions is still very crude or simply not available for the vast majority of CG- force fields. Here, we show that incorporating statistical data retrieved from the Protein Data Bank (PDB) to set specific Lennard-Jones interactions can produce structurally accurate CG molecular dynamics simulations. Using this simple approach, we provide a set of interaction parameters for Calcium, Magnesium, and Zinc ions, which cover more than 80% of the metal-bound structures reported on the PDB. Simulations performed using the SIRAH force field on several proteins and DNA systems show that using the present approach it is possible to obtain non-bonded interaction parameters that obviate the use of topological constraints. </p>

scholarly journals Vision based hardware-software real-time control system for autonomous landing of an UAV

2020 ◽  
Author(s):  
Krzysztof Blachut ◽  
Hubert Szolc ◽  
Mateusz Wasala ◽  
Tomasz Kryjak ◽  
Marek Gorgon
Keyword(s):  
Control System ◽  
Real Time ◽  
Heterogeneous Computing ◽  
Computing System ◽  
Video Stream ◽  
Real Time Control ◽  
Time Data ◽  
Autonomous Landing ◽  
Computing Platform ◽  
Time Control

In this paper we present a vision based hardware-software control system enabling autonomous landing of a mul-tirotor unmanned aerial vehicle (UAV). It allows the detection of a marked landing pad in real-time for a 1280 x 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption.

Gradual GRAM and secure computation for RAM programs

2021 ◽  
pp. 1-33
Author(s):  
Carmit Hazay ◽  
Mor Lilintal
Keyword(s):  
Secure Computation ◽  
Boolean Circuits ◽  
Conceptual Similarity ◽  
Garbled Circuits ◽  
Security Guarantees

Despite the fact that the majority of applications encountered in practice today are captured more efficiently by RAM programs, the area of secure two-party computation (2PC) has seen tremendous improvement mostly for Boolean circuits. One of the most studied objects in this domain is garbled circuits. Analogously, garbled RAM (GRAM) provide similar security guarantees for RAM programs with applications to constant round 2PC. In this work we consider the notion of gradual GRAM which requires no memory garbling algorithm. Our approach provides several qualitative advantages over prior works due to the conceptual similarity to the analogue garbling mechanism for Boolean circuits. We next revisit the GRAM construction from (In STOC (2015) 449–458) and improve it in two orthogonal aspects: match it directly with tree-based ORAMs and explore its consistency with gradual ORAM.

scholarly journals Coarse-Grained Models Reveal Functional Dynamics - I. Elastic Network Models – Theories, Comparisons and Perspectives

2008 ◽  
Vol 2 ◽  
pp. BBI.S460 ◽  
Author(s):  
Lee-Wei Yang ◽  
Choon-Peng Chng
Keyword(s):  
Conformational Changes ◽  
Conformational Dynamics ◽  
Computational Cost ◽  
Network Models ◽  
Coarse Graining ◽  
Coarse Grained ◽  
Elastic Network ◽  
Elastic Network Models ◽  
Time Space ◽  
Functional Dynamics

In this review, we summarize the progress on coarse-grained elastic network models (CG-ENMs) in the past decade. Theories were formulated to allow study of conformational dynamics in time/space frames of biological interest. Several highlighted models and their underlined hypotheses are introduced in physical depth. Important ENM offshoots, motivated to reproduce experimental data as well as to address the slow-mode-encoded configurational transitions, are also introduced. With the theoretical developments, computational cost is significantly reduced due to simplified potentials and coarse-grained schemes. Accumulating wealth of data suggest that ENMs agree equally well with experiment in describing equilibrium dynamics despite their distinct potentials and levels of coarse-graining. They however do differ in the slowest motional components that are essential to address large conformational changes of functional significance. The difference stems from the dissimilar curvatures of the harmonic energy wells described for each model. We also provide our views on the predictability of ‘open to close’ (open→close) transitions of biomolecules on the basis of conformational selection theory. Lastly, we address the limitations of the ENM formalism which are partially alleviated by the complementary CG-MD approach, to be introduced in the second paper of this two-part series.

scholarly journals MiNgMatch—A Fast N-gram Model for Word Segmentation of the Ainu Language

Information ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 317 ◽  
Author(s):  
Karol Nowakowski ◽  
Michal Ptaszynski ◽  
Fumito Masui
Keyword(s):  
Language Processing ◽  
High Performance ◽  
Computational Cost ◽  
Neural Model ◽  
Word Segmentation ◽  
Coarse Grained ◽  
Endangered Language ◽  
Modelling Techniques ◽  
Series Of Experiments ◽  
N Gram

Word segmentation is an essential task in automatic language processing for languages where there are no explicit word boundary markers, or where space-delimited orthographic words are too coarse-grained. In this paper we introduce the MiNgMatch Segmenter—a fast word segmentation algorithm, which reduces the problem of identifying word boundaries to finding the shortest sequence of lexical n-grams matching the input text. In order to validate our method in a low-resource scenario involving extremely sparse data, we tested it with a small corpus of text in the critically endangered language of the Ainu people living in northern parts of Japan. Furthermore, we performed a series of experiments comparing our algorithm with systems utilizing state-of-the-art lexical n-gram-based language modelling techniques (namely, Stupid Backoff model and a model with modified Kneser-Ney smoothing), as well as a neural model performing word segmentation as character sequence labelling. The experimental results we obtained demonstrate the high performance of our algorithm, comparable with the other best-performing models. Given its low computational cost and competitive results, we believe that the proposed approach could be extended to other languages, and possibly also to other Natural Language Processing tasks, such as speech recognition.

Fast Electrostatic Force and Moment Calculations in Multibody-Based Simulations of Coarse-Grained Biopolymers

2011 ◽  
Author(s):  
Mohammad Poursina ◽  
Jeremy Laflin ◽  
Kurt S. Anderson
Keyword(s):  
Force Field ◽  
Long Range ◽  
Electrostatic Force ◽  
Center Of Mass ◽  
Computational Cost ◽  
Field Approximation ◽  
The Body ◽  
Divide And Conquer ◽  
Coarse Grained ◽  
Coarse Grain

In molecular simulations, the dominant portion of the computational cost is associated with force field calculations. Herein, we extend the approach used to approximate long range gravitational force and the associated moment in spacecraft dynamics to the coulomb forces present in coarse grained biopolymer simulations. We approximate the resultant force and moment for long-range particle-body and body-body interactions due to the electrostatic force field. The resultant moment approximated here is due to the fact that the net force does not necessarily act through the center of mass of the body (pseudoatom). This moment is considered in multibody-based coarse grain simulations while neglected in bead models which use particle dynamics to address the dynamics of the system. A novel binary divide and conquer algorithm (BDCA) is presented to implement the force field approximation. The proposed algorithm is implemented by considering each rigid/flexible domain as a node of the leaf level of the binary tree. This substructuring strategy is well suited to coarse grain simulations of chain biopolymers using an articulated multibody approach.

scholarly journals Accelerate Scientific Deep Learning Models on Heterogeneous Computing Platform with FPGA

2020 ◽  
Vol 245 ◽  
pp. 09014
Author(s):  
Chao Jiang ◽  
David Ojika ◽  
Sofia Vallecorsa ◽  
Thorsten Kurth ◽  
Prabhat ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Heterogeneous Computing ◽  
Scientific Data ◽  
Great Promise ◽  
University Of Florida ◽  
Computing Platform ◽  
Scientific Data Analysis ◽  
The University ◽  
Heterogeneous Computing Platform

AI and deep learning are experiencing explosive growth in almost every domain involving analysis of big data. Deep learning using Deep Neural Networks (DNNs) has shown great promise for such scientific data analysis applications. However, traditional CPU-based sequential computing without special instructions can no longer meet the requirements of mission-critical applications, which are compute-intensive and require low latency and high throughput. Heterogeneous computing (HGC), with CPUs integrated with GPUs, FPGAs, and other science-targeted accelerators, offers unique capabilities to accelerate DNNs. Collaborating researchers at SHREC1at the University of Florida, CERN Openlab, NERSC2at Lawrence Berkeley National Lab, Dell EMC, and Intel are studying the application of heterogeneous computing (HGC) to scientific problems using DNN models. This paper focuses on the use of FPGAs to accelerate the inferencing stage of the HGC workflow. We present case studies and results in inferencing state-of-the-art DNN models for scientific data analysis, using Intel distribution of OpenVINO, running on an Intel Programmable Acceleration Card (PAC) equipped with an Arria 10 GX FPGA. Using the Intel Deep Learning Acceleration (DLA) development suite to optimize existing FPGA primitives and develop new ones, we were able accelerate the scientific DNN models under study with a speedup from 2.46x to 9.59x for a single Arria 10 FPGA against a single core (single thread) of a server-class Skylake CPU.

Sign in / Sign up

Export Citation Format

Share Document