On a novel press system with six links for precision deep drawing

The objective of this paper is to propose a novel press system with six links for precision deep drawing, and verify its feasibility. First, the system is presented and its advantages are discussed. Then, the kinematic analysis of the system is obtained by examining the geometry of the design. Moreover, kinematic dimensions of the variable coupling are found by using optimization method. Furthermore, the proposed approach is illustrated by a design example, its solid model for the proposed design is established, and then kinematic simulation is performed by ADAMS software. Finally, a prototype and an experimental setup are established, and the experiment is conducted. The results show that the proposed new mechanism is feasible and of reasonable accuracy. In addition, it has the advantages of easy manufacture, lower cost, higher precision, and easy adjustability.     

POPS: an off-peak precomputing scheme for privacy-preserving computing

Emerging privacy-preserving technologies help protect sensitive data during application executions. Recently, the secure two-party computing (TPC) scheme has demonstrated its potential, especially for the secure model inference of a deep learning application by protecting both the user input data and the model parameters. Nevertheless, existing TPC protocols incur excessive communications during the program execution, which lengthens the execution time. In this work, we propose the precomputing scheme, POPS, to address the problem, which is done by shifting the required communications from during the execution to the time prior to the execution. Particular, the multiplication triple generation is computed beforehand with POPS to remove the overhead at runtime. We have analyzed the TPC protocols to ensure that the precomputing scheme conforms the existing secure protocols. Our results show that POPS takes a step forward in the secure inference by delivering up to \(20\times \) and \(5\times \) speedups against the prior work for the microbenchmark and the convolutional neural network experiments, respectively.

     

A Halide-based Synergistic Computing Framework for Heterogeneous Systems

Journal of Signal Processing Systems - New programming models have been developed to embrace contemporary heterogeneous machines, each of which may contain several types of processors, e.g., CPUs,...     

Continuous distance-based skyline queries in road networks

In recent years, the research community has introduced various methods for processing skyline queries in road networks. A skyline query retrieves the skyline points that are not dominated by others in terms of static and dynamic attributes (i.e., the road distance). This paper addresses the issue of efficiently processing continuous skyline queries in road networks. Two novel and important distance-based skyline queries are presented, namely, the continuous  _dε-skyline$d_{\epsilon }\mathrm{-}\mathit{skyline}$query   (_Cdε-SQ${\mathit{Cd}}_{\epsilon }\mathrm{-}\mathit{SQ}$) and the continuous k nearest neighbor-skyline query (Cknn-SQ  ). A grid index is first designed to effectively manage the information of data objects and then two algorithms are proposed, the _Cdε-SQ${\mathit{Cd}}_{\epsilon }\mathrm{-}\mathit{SQ}$algorithm   and the _Cdε-SQ⁺${\mathit{Cd}}_{\epsilon }\mathrm{-}{\mathit{SQ}}^{+}$algorithm  , which are combined with the grid index to answer the _Cdε-SQ${\mathit{Cd}}_{\epsilon }\mathrm{-}\mathit{SQ}$. Similarly, the Cknn-SQ algorithm and the Cknn-SQ⁺algorithm are developed to efficiently process the Cknn-SQ. Extensive experiments using real road network datasets demonstrate the effectiveness and the efficiency of the proposed algorithms.     

On designing the adaptive computation framework of distributed deep learning models for Internet-of-Things applications

The Journal of Supercomputing - Deep learning methods have been gradually adopted in the Internet-of-Things (IoT) applications. Nevertheless, the large demands for the computation and memory...     

GPUBlocks: GUI Programming Tool for CUDA and OpenCL

Recent advances in general-purpose graphics processing units (GPGPUs) have resulted in massively parallel hardware that is widely available to achieve high performance in desktop, notebook, and even mobile computer systems. While multicore technology has become the norm of modern computers, programming such systems requires the understanding of underlying hardware architecture and hence posts a great challenge for average programmers, who might be professionals in specific domains, but not experts in parallel programming. This paper presents a GUI tool called GPUBlocks that can facilitate parallel programming on multicore computer systems. GPUBlocks is developed based on the OpenBlocks framework, an extendable tool for graphical programming, to construct the GUI-based programming environment for CUDA and OpenCL parallel computing platforms. Programmers simply need to drag-n-drop blocks, fill the fields of the blocks, and connect them according to array or matrix computations that are specified by algorithms. GPUBlocks can then translate block-based code to CUDA or OpenCL programs. Furthermore, a couple of optimization constructs have also been offered for rapid program optimization. Experimental results have shown that the generated CUDA and OpenCL programs can achieve reasonable speedups on GPUs. Consequently, GPUBlocks can be used as a tool for fast prototyping of GPU applications or a platform for educational parallel programming.

     

Rapid Hybrid Simulation Methods for Exploring the Design Space of Signal Processors with Dynamic and Scalable Timing Models

Journal of Signal Processing Systems - As today’s state-of-the-art signal processing systems often require heterogeneous computing and special-purpose accelerators to offer highly efficient...     

Task Scheduling for Context Minimization in Dynamically Reconfigurable Platforms

Dynamically reconfigurable hardware provides useful means to reduce the time-to-prototype and even the time-to-market in product designs. It also offers a good alternative in reconfiguring hardware logics to optimize the system performance. This paper targets an essential issue in reconfigurable computing, i.e., the minimization of configuration contexts. We explore different constraints on the CONTEXT MINIMIZATION problem. When the resulting subproblems are polynomial-time solvable, optimal algorithms are presented. We also present a greedy algorithm for the CONTEXT MINIMIZATION problem, that is proved NP\mathcal{NP}-complete. The capability of the proposed algorithm is evaluated by a series of experiments.     

Message-Passing Programming for Embedded Multicore Signal-Processing Platforms

Recently, embedded multicore platforms have become popular for signal processing, but software development for such platforms is still very slow. First, parallel programming is more challenging than sequential programming to average programmers. To make the problem worse, software is not portable among the platforms, since each multicore signal-processing platform offers its own programming interface/language. We believe this problem can be relieved by adding the support of a standard message-passing programming to embedded multicore platforms. In particular, we would like to leverage MPI, the most successful message-passing system, which practically enables the development of portable applications to run on many parallel machines. There are technical challenges to support MPI on embedded multicore platforms: the size of the library, architecture issues, and performance issues. This paper identifies and addresses these issues. To enable the reuse of existing MPI programs and make message-passing programming portable and efficient, we designed a light-weight MPI-like message-passing library with a three-layer modular design, where the top two layers are mostly platform-independent, and the bottom layer enables platform-specific optimizations. This approach has allowed us to effectively support message-passing on several popular embedded multicore signal-processing platforms, including the IBM CELL and the ITRI PAC Duo. Our results show that message-passing programming is a viable solution for multicore signal processing applications and may be considered by platform vendors.