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The Interplay of Online and Offline Machine Learning for Design Flow Tuning

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Machine Learning Applications in Electronic Design Automation

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Abstract

Modern logic and physical synthesis tools provide numerous options and parameters that can drastically affect design quality; however, the large number of options leads to a complex design space difficult for human designers to navigate. Fortunately, machine learning approaches and cloud computing environments are well suited for tackling complex parameter-tuning problems like those seen in VLSI design flows. This chapter proposes a holistic approach where online and offline machine learning approaches work together for tuning industrial design flows. We provide an overview of recent research on design flow tuning, spanning the application domains of high-level synthesis (HLS), field-programmable gate array (FPGA) synthesis and place-and-route, and VLSI logic synthesis and physical design (LSPD). We highlight the industrial design flow tuner SynTunSys (STS) as a case study. This system has been used to optimize multiple high-performance processors. STS consists of an online system that optimizes designs and generates data for a recommender system that performs offline training and recommendation. Experimental results show the collaboration between STS online and offline machine learning systems as well as insight from human designers provides best-of-breed results. Finally, we discuss potential new directions for design flow tuning research.

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Notes

  1. 1.

    We describe only primitives using CAD tool parameters for brevity, although primitives may consist of design flow parameters outside of the CAD tool and include snippets of code to modify the design flow.

  2. 2.

    We consider QoR and PPA synonymous terms, although we use QoR in the STS context since we often tune numerous metrics, beyond performance, power, and area.

  3. 3.

    Typical timing metrics of interest are (i) internal slack, e.g., latch-to-latch slack (L2L), (ii) worst negative slack (WNS), and (iii) total negative slack (TNS).

  4. 4.

    The primitive library and historical primitive performance are two ways the online system improves “offline”; however, a new online system can still be effective without these evolving improvements.

  5. 5.

    The term variables is often referred to as parameters or weights in other machine learning applications and recommender systems. Here, we refer to them as variables to avoid the confusion with the design flow parameters.

  6. 6.

    CP and Tucker are two widely used methods for tensor decomposition. The acronym CP stands for (1) CANDECOMP (canonical decomposition)/PARAFAC (parallel factor analysis) or for (2) canonical polyadic (decomposition). Tucker is a generalization of CP, where the core tensor is not super-diagonal and contains hidden features [27]. With CP, it is possible to explicitly represent the latent information for each macro separately.

  7. 7.

    A primitive is typically categorized based on the expected metrics it will affect, which follows Consideration 3 in Sect. 13.2.1.

  8. 8.

    While, in concept, empirically comparing multiple selection and blending algorithms is feasible, the practical compute effort would be quite expensive.

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Authors and Affiliations

  1. IBM T. J. Watson Research Center, Yorktown Heights, NY, USA

    Matthew M. Ziegler

  2. Department of Computer Science, Columbia University, New York, NY, USA

    Jihye Kwon & Luca P. Carloni

  3. Cadence Design Systems, San Jose, CA, USA

    Hung-Yi Liu

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  1. Matthew M. Ziegler
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  2. Jihye Kwon
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  3. Hung-Yi Liu
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  4. Luca P. Carloni
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Correspondence to Matthew M. Ziegler .

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Editors and Affiliations

  1. NVIDIA, Austin, TX, USA

    Haoxing Ren

  2. Texas A&M University, College Station, TX, USA

    Jiang Hu

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Ziegler, M.M., Kwon, J., Liu, HY., Carloni, L.P. (2022). The Interplay of Online and Offline Machine Learning for Design Flow Tuning. In: Ren, H., Hu, J. (eds) Machine Learning Applications in Electronic Design Automation. Springer, Cham. https://doi.org/10.1007/978-3-031-13074-8_13

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