[ASPLOS] Tutorial Schedule (#1374)

docs/conferenceDescriptions/asplos24TutorialDescription.md

@@ -22,18 +22,19 @@ Prerequisite: please bring your laptop, so that you can ssh into our Ryzen AI en
 
 | Time | Topic | Presenter | Slides or Code |
 |------|-------|-----------|----------------|
-| 08:30am | Intro to spatial compute and explicit data movement | Kristof | tbd |
-| 08:45am | "Hello World" from Ryzen AI | Jack | tbd |
-| 09:00am | Data movement on Ryzen AI with objectFIFOs | Joe | tbd |
-| 09:30am | Exersise 1: Build and run your first program | All | tbd |
-| 09:45am | Exersise 2: Vector-scalar | All |tbd |
+| 08:30am | Intro to spatial compute and explicit data movement | Kristof | [Programming Guide](../../programming_guide/) |
+| 08:45am | "Hello World" from Ryzen AI | Joe | [AI Engine Basic Building Blocks](../../programming_guide/section-1/) |
+| 09:00am | Data movement on Ryzen AI with objectFIFOs | Joe | [Data Movement](../../programming_guide/section-2/) |
+| 09:30am | Your First Program | Kristof | [My First Program](../../programming_guide/section-3) |
+| 09:50am | Exercise 1: Build and run your first program | All | [Passthrough](../../programming_examples/basic/passthrough_kernel/) |
 | 10:00am | Break | | |
-| 11:00am | Tracing and performance analysis | Jack | tbd |
-| 11:10am | Exercise 3: Tracing vector-scalar | All | tbd |
-| 11:30am | Vectorizing on AIE | Kristof | tbd |
-| 11:40am | Exercise 4: Vectorized vector-scalar | All | tbd |
-| 12:00pm | Dataflow and larger designs | Joe | tbd |
-| 12:15pm | Exercises | All | |
+| 10:30am | Exercise 2: Vector-Scalar Mul | All | [Vector Scalar Mul](../../programming_examples/basic/vector_scalar_mul/) |
+| 10:40am | Tracing and performance analysis | Jack | [Timers](../../programming_guide/section-4/section-4a/) and [Tracing](../../programming_guide/section-4/section-4b/) |
+| 11:10am | Exercise 3: Tracing vector-scalar | All | [Vector Scalar Mul](../../programming_examples/basic/vector_scalar_mul/) |
+| 11:30am | Vectorizing on AIE | Jack | [Kernel Vectorization](../../programming_guide/section-4/section-4c/) |
+| 11:40am | Exercise 4: Vectorized vector-scalar | All | [Vector Scalar Mul](../../programming_examples/basic/vector_scalar_mul/) |
+| 12:00pm | Dataflow and larger designs | Joe | [Example Vector Designs](../../programming_guide/section-5/) and [Large Example Designs](../../programming_guide/section-6/) |
+| 12:15pm | Exercises | All | [Programming Examples](../../programming_examples/) |
 | 12:30pm | Close Tutorial | All | |
 
 
@@ -46,3 +47,5 @@ Prerequisite: please bring your laptop, so that you can ssh into our Ryzen AI en
 *Kristof Denolf* is a Fellow in AMD's Research and Advanced Development group where he is working on energy efficient computer vision and video processing applications to shape future AMD devices. He earned a M.Eng. in electronics from the Katholieke Hogeschool Brugge-Oostende (1998), now part of KULeuven, a M.Sc. in electronic system design from Leeds Beckett University (2000) and a Ph.D. from the Technical University Eindhoven (2007). He has over 25 years of combined research and industry experience at IMEC, Philips, Barco, Apple, Xilinx and AMD. His main research interest are all aspects of the cost-efficient and dataflow oriented design of video, vision and graphics systems.
 
 *Phil James-Roxby* is a Senior Fellow in AMD’s Research and Advanced Development group, working on compilers and runtimes to support current and future AMD devices, particularly in the domain on AI processing.  In the past, he has been responsible for a number of software enablement activities for hardware devices, including SDNet and SDAccel at Xilinx, and the original development environement for the AI Engines.  He holds a PhD from the University of Manchester on hardware acceleration of embedded machine learning applications, and his main research interest continues to be how to enable users to efficiently use diverse hardware in heterogenous systems.
+
+*Samuel Bayliss* is a Fellow in the Research and Advanced Development group at AMD. His academic experience includes formative study at Imperial College London, for which he earned MEng and PhD degrees in 2006 and 2012 respectively. He is energized by his current work in advancing compiler tooling using MLIR, developing programming abstractions for parallel compute and evolving hardware architectures for efficient machine learning.