2015Unpublished venueRequires access

Behavioral-level IP integration in high-level synthesis

Liwei Yang, Swathi Gurumani, Deming Chen, Kyle Rupnow

Open publisher page 7 citations

Abstract

High level synthesis (HLS) quality improvements have led to its increased adoption in hardware design. In the design flow, IP reuse is critical for achieving quality of results, yet current HLS tools allow only a small set of tool-provided IPs integrated during HLS. General IP integration is then handled as an additional step either manually or using other system level tools. Performing post-HLS integration of IPs requires a clear separation of IPs from HLS-generated cores, requiring significant partitioning effort. In contrast, behavioral-level IP integration during HLS can simplify the design flow while still supporting HLS-based optimization and design space exploration. In this paper, we develop a general IP integration framework for HLS that supports fixed- and variable-latency IPs without requiring application partitioning. Using this framework that allows user-specified function/instruction-to-IP mapping, we demonstrate integration of both synthesizable and non-synthesizable IPs.

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What this paper is about

High level synthesis (HLS) quality improvements have led to its increased adoption in hardware design. In the design flow, IP reuse is critical for achieving quality of results, yet current HLS tools allow only a small set of tool-provided IPs integrated during HLS. General IP integration is then handled as an additional step either manually or using other system level tools. Performing post-HLS integration of IPs requires a clear separation of IPs from HLS-generated cores, requiring significant partitioning effort. In contrast, behavioral-level IP integration during HLS can simplify the design flow while still supporting HLS-based optimization and design space exploration. In this paper, we develop a general IP integration framework for HLS that supports fixed- and variable-latency IPs without requiring application partitioning. Using this framework that allows user-specified function/instruction-to-IP mapping, we demonstrate integration of both synthesizable and non-synthesizable IPs.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

High level synthesis (HLS) quality improvements have led to its increased adoption in hardware design. In the design flow, IP reuse is critical for achieving quality of results, yet current HLS tools allow only a small set of tool-provided IPs integrated during HLS. General IP integration is then handled as an additional step either manually or using other system level tools. Performing post-HLS integration of IPs requires a clear separation of IPs from HLS-generated cores, requiring significant partitioning effort. In contrast, behavioral-level IP integration during HLS can simplify the design flow while still supporting HLS-based optimization and design space exploration. In this paper, we develop a general IP integration framework for HLS that supports fixed- and variable-latency IPs without requiring application partitioning. Using this framework that allows user-specified function/instruction-to-IP mapping, we demonstrate integration of both synthesizable and non-synthesizable IPs.

Key concepts: High-level synthesis, Design space exploration, Computer science, Design flow, Reuse, Computer architecture, Embedded system, Logic synthesis

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