Schaefer, Benjamin Carrion

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6682

Benjamin Carrion Schaefer joined the UT Dallas faculty in 2016 as an Assistant Professor of Electrical Engineering. His research interests include:

  • VLSI Design
  • Reconfigurable Computing
  • High-Level Synthesis
  • High-Level Synthesis Design Space Exploration
  • Hardware Security
  • C-based SoC Design
  • Approxinate Computing in High-Level Synthesis
  • HW Diversity for Reliability

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Recent Submissions

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    VeriIntel2C: Abstracting RTL to C to Maximize High-Level Synthesis Design Space Exploration
    (Elsevier Science B.V., 2018-08-23) Mahapatra, Anushree; Schaefer, Benjamin Carrion; Schaefer, Benjamin Carrion
    The design of integrated circuits (ICs) is typically done using low level Hardware Description Languages (HDLs) like Verilog or VHDL (Register Transfer Level). These enable the full controllability of the generated hardware design as they allow to specify the detailed behaviour and structure of the architecture, at every single clock cycle. The main drawback of using these low level HDLs is that takes very long time to create and verify large ICs with them. Moreover, it is hard to re-use HDL code for future projects that require changes in the micro-architecture. Thus, the industry is moving the level of abstraction to C-based VLSI design where designers only have to specify the functionality of the program and High-Level Synthesis (HLS) tools generate the HDL automatically. One additional benefit of C-based VLSI design is that it enables to explore the search space of possible micro-architectures from a single behavioral description. The result of a Design Space Exploration (DSE) is a trade-off curve of Pareto-optimal designs with unique area vs. performance metrics. Most VLSI design companies have large amounts of legacy HDL code. Thus, it makes sense to have an automatic flow to convert HDL designs into behavioral descriptions (e.g. C, C++ or SystemC) optimized for HLS DSE. This implies that the generation of explorable constructs, e.g. loops and arrays, which upon exploration, lead to very different micro-architectures (e.g. loops can be unrolled or folded, arrays can be mapped to RAMs or registers). In this paper, we propose a robust RTL to C translation method called Verilntel2C to abstract RTL descriptions (written in Verilog) into ANSI-C descriptions optimized for HLS DSE by generating a large number of loops and arrays. Our method is able to generate these explorable constructs with the use of extended Hardware Petri Nets to extract the behaviour of the Verilog designs and to generate a Control Data Flow Graph (CDFG) that allows the easy identification of these constructs. From the experimental results, we are able to demonstrate that Verilntel2C expands the design space considerably and also improves the quality of design space by 55% on average compared to previous work, on a wide range of designs.
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    Partial Encryption of Behavioral IPs to Selectively Control the Design Space in High-Level Synthesis
    (Institute of Electrical and Electronics Engineers Inc., 2019-03) Wang, Zi; Schaefer, Benjamin Carrion; Wang, Zi; Schaefer, Benjamin Carrion
    Commercial High-Level Synthesis (HLS) tool vendors have started to enable ways to protect Behavioral IP (BIPs) from being unlawful used. The main approach is to provide tools to encrypt these BIPs which can be decrypted by the HLS tool only. The main problem with this approach is that encrypting the IP does not allow BIP users to insert synthesis directives into the source code in the form of pragmas (comments), and hence cancels out one of the most important advantages of C-based VLSI design: The ability to automatically generate micro-architectures with unique design metrics, e.g. area, power and performance. This work studies the impact to the search space when synthesis directives are not able to be inserted in to the encrypted IP source code while other options are still available to the BIP users (e.g. setting global synthesis options and limiting the number and type of functional units) and proposes a method that selectively controls the search space by encrypting different portions of the BIP. To achieve this goal we propose a fast heuristic based on divide and conquer method. Experimental results show that our proposed method works well compared to an exhaustive search that leads to the optimal solution. © 2019 EDAA.
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    Common-Mode Failure Mitigation: Increasing Diversity Through High-Level Synthesis
    (Institute of Electrical and Electronics Engineers Inc., 2019-03-25) Taher, Farah Naz; Joslin, Matthew; Balachandran, A.; Zhu, Zhiqi; Schaefer, Benjamin Carrion; Taher, Farah Naz; Joslin, Matthew; Zhu, Zhiqi; Schaefer, Benjamin Carrion
    Fault tolerance is vital in many domains. One popular way to increase fault-tolerance is through hardware redundancy. However, basic redundancy cannot cope with Common Mode Failures (CMFs). One way to address CMF is through the use of diversity in combination with traditional hardware redundancy. This work proposes an automatic design space exploration (DSE) method to generate optimized redundant hardware accelerators with maximum diversity to protect against CMFs given as a single behavioral description for High-Level Synthesis (HLS). For this purpose, this work exploits one of the main advantages of C-based VLSI design over the traditional RT-level design based on low-level Hardware Description Languages (HDLs): The ability to generate micro-architectures with unique characteristics from the same behavioral description. Experimental results show that the proposed method provides a significant diversity increment compared to using traditional RTL-based exploration to generate diverse designs. © 2019 EDAA.
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    Design Obfuscation through Selective Post-Fabrication Transistor-Level Programming
    (Institute of Electrical and Electronics Engineers Inc., 2019-03-25) Shihab, Mustafa M.; Tian, Jingxiang; Reddy, Gaurav Rajavendra; Hu, Bo; Swartz, William; Schaefer, Benjamin Carrion; Sechen, Carl; Makris, Yiorgos; 110195631 (Sechen, CM); Shihab, Mustafa M.; Tian, Jingxiang; Reddy, Gaurav Rajavendra; Hu, Bo; Swartz, William; Schaefer, Benjamin Carrion; Sechen, Carl; Makris, Yiorgos
    Widespread adoption of the fabless business model and utilization of third-party foundries have increased the exposure of sensitive designs to security threats such as intellectual property (IP) theft and integrated circuit (IC) counterfeiting. As a result, concerted interest in various design obfuscation schemes for deterring reverse engineering and/or unauthorized reproduction and usage of ICs has surfaced. To this end, in this paper we present a novel mechanism for structurally obfuscating sensitive parts of a design through post-fabrication TRAnsistor-level Programming (TRAP). We introduce a transistor-level programmable fabric and we discuss its unique advantages towards design obfuscation, as well as a customized CAD framework for seamlessly integrating this fabric in an ASIC design flow. We theoretically analyze the complexity of attacking TRAP-obfuscated designs through both brute-force and intelligent SAT-based attacks and we present a silicon implementation of a platform for experimenting with TRAP. Effectiveness of the proposed method is evaluated through selective obfuscation of various modules of a modern microprocessor design. Results corroborate that, as compared to an FPGA implementation, TRAP-based obfuscation offers superior resistance against both brute-force and oracle-guided SAT attacks, while incurring an order of magnitude less area, power and delay overhead. © 2019 EDAA.
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    A Machine Learning Based Hard Fault Recuperation Model for Approximate Hardware Accelerators
    (The Association for Computing Machinery) Taher, Farah Naz; Callenes-Sloan, J.; Schaefer, Benjamin Carrion; Taher, Farah Naz; Schaefer, Benjamin Carrion
    Continuous pursuit of higher performance and energy efficiency has led to heterogeneous SoC that contains multiple dedicated hardware accelerators. These accelerators exploit the inherent parallelism of tasks and are often tolerant to inaccuracies in their outputs, e.g. image and digital signal processing applications. At the same time, permanent faults are escalating due to process scaling and power restrictions, leading to erroneous outputs. To address this issue, in this paper, we propose a low-cost, universal fault recovery/repair method that utilizes supervised machine learning techniques to ameliorate the effect of permanent fault(s) in hardware accelerators that can tolerate inexact outputs. The proposed compensation model does not require any information about the accelerator and is highly scalable with low area overhead. Experimental results show, the proposed method improves the accuracy by 50% and decreases the overall mean error rate by 90% with an area overhead of 5% compared to execution without fault compensation.

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