fixing typo

README.md

@@ -242,7 +242,7 @@ cmr "reproduce paper micro-2023 victima _run"
 
 ### CM presentations
 
-* MLPerf-Bench @ HPCA'23 *"Automatically Composing High-Performance and Cost-effective AI Systems with MLCommons' CM and MLPerf"*: [ [slides](https://doi.org/10.5281/zenodo.10786893) ]
+* MLPerf-Bench @ HPCA'24 *"Automatically Composing High-Performance and Cost-effective AI Systems with MLCommons' CM and MLPerf"*: [ [slides](https://doi.org/10.5281/zenodo.10786893) ]
 * ACM REP'23 keynote about MLCommons CM: [ [slides](https://doi.org/10.5281/zenodo.8105339) ] [ [YouTube](https://youtu.be/_1f9i_Bzjmg) ]
 * ACM TechTalk'21 about automating research projects: [ [YouTube](https://www.youtube.com/watch?v=7zpeIVwICa4) ] [ [slides](https://learning.acm.org/binaries/content/assets/leaning-center/webinar-slides/2021/grigorifursin_techtalk_slides.pdf) ]
 * MLPerf inference submitter orientation: [ [v4.0 slides]( https://doi.org/10.5281/zenodo.10605079 ) ] [ [v3.1 slides](https://doi.org/10.5281/zenodo.8144274) ]

docs/taskforce.md

@@ -12,6 +12,7 @@
 * Continuously encode MLPerf rules and best practices in the [CM automation recipes and workflows for MLPerf](https://github.com/mlcommons/ck/tree/master/cm-mlops/script)
   to reduce the burden for submitters to go through numerous README files 
   and track all the latest changes and updates.
+* [Automatically compose high-performance and cost-efficient AI applications and systems using MLPerf and CM](https://doi.org/10.5281/zenodo.10786893).
 
 ## Chairs and Tech Leads