Cliff Sze

Cliff Sze

His research interests include optimization and the analysis of algorithms.
Authored Publications
Google Publications
Other Publications
Sort By
  • Title
  • Title, descending
  • Year
  • Year, descending
    Large-Scale 3D Chips: Challenges and Solutions for Design Automation, Testing, and Trustworthy Integration
    Johann Knechtel
    Ozgur Sinanoglu
    Ibrahim (Abe) M. Elfadel
    Jens Lienig
    IPSJ Trans. System LSI Design Methodology(2017)
    Preview abstract Three-dimensional (3D) integration of electronic chips has been advocated by both industry and academia for many years. It is acknowledged as one of the most promising approaches to meet ever-increasing demands on performance, functionality, and power consumption. Furthermore, 3D integration has been shown to be most effective and efficient once large-scale integration is targeted for. However, a multitude of challenges has thus far obstructed the mainstream transition from “classical 2D chips” to such large-scale 3D chips. In this paper, we survey all popular 3D integration options available and advocate that using an interposer as system-level integration backbone would be the most practical for large-scale industrial applications and design reuse. We review major design (automation) challenges and related promising solutions for interposer-based 3D chips in particular, among the other 3D options. Thereby we outline (i) the need for a unified workflow, especially once full-custom design is considered, (ii) the current design-automation solutions and future prospects for both classical (digital) and advanced (heterogeneous) interposer stacks, (iii) the state-of-art and open challenges for testing of 3D chips, and (iv) the challenges of securing hardware in general and the prospects for large-scale and trustworthy 3D chips in particular. View details
    Fast and Highly Scalable Bayesian MDP on a GPU Platform
    He Zhou
    Sunil P. Khatri
    Jiang Hu
    Frank Liu
    ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), ACM(2017)
    Preview abstract By employing Optimal Bayesian Robust (OBR), Bayesian Markov Decision Process (BMDP) can be a power optimization method to solve large problems. However, due to the “curse of dimensionality”, the data storage limitation hinders the practical application of BMDP. To overcome this impediment, we propose a novel Improved Compressed Sparse Row (ICSR) data structure in this paper, and developed the implementation of BMDP solver with ICSR technique on a heterogeneous platform with GPU. The simulation results demonstrate that our techniques achieve about a 5× reduction in memory utilization over using full matrix, and an average speedup of 4.1× over using full matrix. Additionally, we present a study of the tradeoff between the runtime and the trends of result difference between our ICSR techniques and using full matrix. View details
    No Results Found