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			<titleStmt><title level='a'>ScaleHLS: a scalable high-level synthesis framework with multi-level transformations and optimizations: invited</title></titleStmt>
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				<publisher>ACM</publisher>
				<date>07/10/2022</date>
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				<bibl> 
					<idno type="par_id">10477705</idno>
					<idno type="doi">10.1145/3489517.3530631</idno>
					
					<author>Hanchen Ye</author><author>HyeGang Jun</author><author>Hyunmin Jeong</author><author>Stephen Neuendorffer</author><author>Deming Chen</author>
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			<abstract><ab><![CDATA[This paper presents an enhanced version of a scalable HLS (High-Level Synthesis) framework named ScaleHLS, which can compile HLS C/C++ programs and PyTorch models to highly-efficient and synthesizable C++ designs. The original version of ScaleHLS achieved significant speedup on both C/C++ kernels and PyTorch models [14]. In this paper, we first highlight the key features of ScaleHLS on tackling the challenges present in the representation, optimization, and exploration of large-scale HLS designs. To further improve the scalability of ScaleHLS, we then propose an enhanced HLS transform and analysis library supported in both C++ and Python, and a new design space exploration algorithm to handle HLS designs with hierarchical structures more effectively. Comparing to the original ScaleHLS, our enhanced version improves the speedup by up to 60.9× on FPGAs. ScaleHLS is fully open-sourced at https://github.com/hanchenye/scalehls.]]></ab></abstract>
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