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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20240626T180033Z
LOCATION:2008\, 2nd Floor
DTSTART;TZID=America/Los_Angeles:20240624T113000
DTEND;TZID=America/Los_Angeles:20240624T114500
UID:dac_DAC 2024_sess183_BED130@linklings.com
SUMMARY:Clock parameter tuning with an intelligent adaptive learning to im
 prove performance and power of Multisource Clock Tree Synthesis
DESCRIPTION:Back-End Design\n\nDivyarajsinh Vaghela, Jagadeesh Gnanasekara
 n, and Gaurav Bhatia (Intel Corporation)\n\nClock parameter tuning and opt
 imization of clock networks in lower technology nodes is one of the challe
 nges in Backend design. Every design has its own unique challenges and com
 ing out with a fixed recipe without understanding design models seems impr
 actical. Identifying best recipe that affect timing and some additional pa
 rameters that may improve timing cannot be tested in a short amount of tim
 e and it is a time-consuming process that requires significant effort from
  the design engineer. Intelligent Adaptive learning tool capability gives 
 us the best way to sweep multiple parameters in an efficient way to tune r
 ecipes to achieve better Performance and Power. This capability is used to
  free the design engineer from the drudgery of manual search, comparing an
 d learning. This approach provides an explanation to the user that justifi
 es its recommendations, decision, or action. The user decides based on the
  explanation and it helps designer to identify right knobs which are givin
 g best results in terms of timing and power. Adaptive learning approach al
 lowing tool to explore all possibilities within predefined user inputs/obj
 ectives to achieve best QoR. The clock methodology is used for evaluation 
 is Multisource Clock Tree Synthesis (MSCTS).This presentation investigates
  comparative studies of Conventional CTS, MSCTS and MSCTS with Adaptive Le
 arning tool and summarizes improvements that have been achieved in clock Q
 OR.\n\nTopic: Back-End Design, Design, Engineering Tracks\n\nSession Chair
 : Gaurav Kumar Varshney (Texas Instruments)
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