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+in+generating+Register+Transfer+Level+(RTL)+code%252C+with+notable+advancements+showcased+by+commercial+models+such+as+GPT-4+and+Claude3-Opus.+However%252C+these+proprietary+LLMs+often+raise+concerns+regarding+privacy+and+security.+While+open-source+LLMs+offer+solutions+to+these+concerns%252C+they+typically+underperform+commercial+models+in+RTL+code+generation+tasks%252C+primarily+due+to+the+scarcity+of+high-quality+open-source+RTL+datasets.+To+address+this+challenge%252C+we+introduce+OriGen+%252C+a+fully+open-source+framework+that+incorporates+self-reflection+capabilities+and+a+novel+dataset+augmentation+methodology+for+generating+high-quality%252C+large-scale+RTL+code.+Our+approach+employs+a+code-tocode+augmentation+technique+to+enhance+the+quality+of+open-source+RTL+code+datasets.+Furthermore%252C+OriGen+can+rectify+syntactic+errors+through+a+self-reflection+process+that+leverages+compiler+feedback.+Experimental+results+demonstrate+that+OriGen+significantly+outperforms+other+open-source+alternatives+in+RTL+code+generation.+It+surpasses+the+previous+best-performing+open-source+LLM+by+12.8%2525+and+even+exceeds+GPT-4+Turbo+in+the+pass%25401+metric+on+the+VerilogEval-Human+benchmark.+Moreover%252C+OriGen+exhibits+superior+capabilities+in+self-reflection+and+error+correction%252C+outperforming+GPT-4+by+19.9%2525+on+a+benchmark+designed+to+evaluate+self-reflection+capabilities.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.baabee89f1fe36162a4287aa56ae6710%3frik%3dQZyvr%252bVTGWGdMw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=RTL+Code+Comment+Section&FORM=IRPRST&ck=52FFEFBF504A35E2DF72CF5BED978B33&selectedIndex=18&itb=0)
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