diff --git a/hw_acceleration.qmd b/hw_acceleration.qmd
index 52a1175e..9cb846f0 100644
--- a/hw_acceleration.qmd
+++ b/hw_acceleration.qmd
@@ -75,20 +75,12 @@ Explanation: Discussing emerging technologies and trends, this section offers re
 
 - Optimization Techniques for New Hardware
 - Flexible Electronics
-    - Case Study 1: AI on Flexible Electronics in Wearables
 - Neuromorphic Computing
-    - Case Study 2: Neuromorphic Computing in Vision Systems
 - In-Memory Computing 
 - ...
 - Challenges with Scalability and Hardware-Software Integration
 - Next-Generation Hardware Trends and Innovations
 
-## Case Studies
-
-Explanation: Real-world case studies offer invaluable insights into the practical applications and challenges of implementing AI on these emerging hardware platforms. This section bridges theoretical knowledge with practical applications.
-
-- Real-world Applications of Emerging Hardware in AI
-
 ## Conclusion
 
 Explanation: This section consolidates the key learnings from the chapter, providing a summary and a future outlook on hardware acceleration in embedded AI systems. This offers insight into where the field might be headed, helping to inspire future projects or study.