Merge branch 'asplos' into asplos-jlo-readme7

.github/workflows/buildAndTestRyzenAI.yml

@@ -127,6 +127,7 @@ jobs:
           python -m venv aie-venv
           source aie-venv/bin/activate
           pip install -r python/requirements.txt
+          pip install -r python/requirements_ml.txt
           pip install jupyter
           sed -i.bak 's/OUTPUT_TIMEOUT = 10/OUTPUT_TIMEOUT = 100/g' \
             $(python -c 'import site; print(site.getsitepackages()[0])')/jupyter_client/runapp.py

aie_kernels/README.md

@@ -0,0 +1,57 @@
+<!---//===- README.md --------------------------*- Markdown -*-===//
+//
+// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+// Copyright (C) 2022, Advanced Micro Devices, Inc.
+// 
+//===----------------------------------------------------------------------===//-->
+
+# AIE Kernels
+
+These kernels are provided as example building blocks for larger designs, and also as illustrations of how to write single core programs for AIEs which can then be duplicated or mixed into multi-core designs using the structural IRON API.
+
+In some cases, the kernels are just generic C code, and will run on any family of AI Engines with varying performance.  Other kernels are then optimized for the AIE1 and AIE2 architectures.  Finally, some kernels use the AIE API, which is a C++ header-only library providing types and operations that get translated into efficient low-level intrinsics, and whose documentation can be found [here](https://www.xilinx.com/htmldocs/xilinx2023_2/aiengine_api/aie_api/doc/index.html), while others use the architecture specific low-level intrinsics directly
+
+> **NOTE:** this set of AIE kernels are meant for demonstration along with the programming examples. The goal is not to be 100% performant, there may be room for further improvement. The kernels are provided as-is with no guarantees of support of AMD or AMD Research and Advanced Development.
+
+## Generic
+| Class | Name | Coding style | Purpose | Datatypes |
+|-|-|-|-|-|
+| basic | [passThrough.cc](./generic/passThrough.cc) | AIE API | A simple memcpy operation | `uint8_t`, `int16_t`, `int32_t` |
+
+## AIE1
+| Name | Coding style | Purpose |
+|-|-|-|
+
+## AIE2
+| Class | Name | Coding style | Purpose | Datatypes |
+|-|-|-|-|-|
+| basic | [zero.cc](../../aie_kernels/aie2/zero.cc) | AIE API | Fill a tensor with zeroes | template |
+| basic | [add.cc](../../aie_kernels/aie2/add.cc) | AIE API | Pointwise addition of 2 tensors | `bfloat16` |
+| basic | [mul.cc](../../aie_kernels/aie2/mul.cc) | AIE API | Pointwise multiplication of 2 tensors | `bfloat16` |
+| basic | [scale.cc](../../aie_kernels/aie2/scale.cc) | AIE API | Scale all elements of a tensor with a scale factor | `int32_t` |
+| basic | [bitwiseOR.cc](../../aie_kernels/aie2/bitwiseOR.cc) | AIE API | Bitwise OR of fixed point tensors | `uint8_t`,`int16_t`,`int32_t`|
+| basic | [bitwiseAND.cc](../../aie_kernels/aie2/bitwiseAND.cc) | AIE API | Bitwise AND of fixed point tensors | `uint8_t`,`int16_t`,`int32_t` |
+| gemm  | [mm.cc](../../aie_kernels/aie2/mm.cc) | AIE API | Matrix/Matrix multiplication | `int16_t`,`bfloat16_t` |
+| gemm  | [mv.cc](../../aie_kernels/aie2/mv.cc) | AIE API | Matrix/Vector multiplication | `bfloat16_t` |
+| |
+| reduction | [reduce_add.cc](../../aie_kernels/aie2/reduce_add.cc) | Intrinsics | Find the sum of elements in a tensor | `int32 _t` |
+| reduction| [reduce_max.cc](../../aie_kernels/aie2/reduce_max.cc) | Intrinsics | Find max value across a tensor | `int32 _t` |
+| reduction | [reduce_min.cc](../../aie_kernels/aie2/reduce_min.cc) | Intrinsics | Find min value across a tensor | `int32 _t` |
+||
+| ml | [conv2dk1_i8.cc](../../aie_kernels/aie2/conv2dk1_i8.cc) | AIE API | 1x1 Conv2D | `int8_t` |
+| ml | [conv2dk1.cc](../../aie_kernels/aie2/conv2dk1.cc) | AIE API | 1x1 Conv2D with fused ReLU | `int8_t`, `uint8_t` |
+| ml | [conv2dk3.cc](../../aie_kernels/aie2/conv2dk3.cc) | AIE API | 3x3 Conv2D with fused ReLU | `int8_t`, `uint8_t` |
+| ml | [conv2dk1_skip.cc](../../aie_kernels/aie2/conv2dk1_skip.cc) | AIE API| 1x1 Conv2D with fused skip addition | `int8_t`, `uint8_t` |
+| ml | [conv2dk1_skip_init.cc](../../aie_kernels/aie2/conv2dk1_skip_init.cc) | AIE API | 1x1 Conv2D with fused 1x1 Conv2D skip addition | `int8_t`, `uint8_t` |
+| ml |[relu.cc](../../aie_kernels/aie2/relu.cc) | Intrinsics | ReLU activation function | `bfloat16_t` |
+| ml |  [bf16_exp.cc](../../aie_kernels/aie2/bf16_exp.cc) | AIE API | Raise all elements in a `bfloat` tensor to $e^x$ | `bfloat16_t` |
+| |
+| vision | [gray2rgba.cc](../../aie_kernels/aie2/gray2rgba.cc) | AIE API | Convert from grayscale to RGBA format | `uint8_t` |
+| vision |[rgba2gray.cc](../../aie_kernels/aie2/rgba2gray.cc) | AIE API | Convert from RGBA format to grayscale | `uint8_t` |
+| vision | [rgba2hue.cc](../../aie_kernels/aie2/rgba2hue.cc) | AIE API | Convert from RGBA to hue | `uint8_t` |
+| vision | [addWeighted.cc](../../aie_kernels/aie2/addWeighted.cc) | AIE API | Fixed point weighted sum of two tensors | `uint8_t` |
+| vision | [threshold.cc](../../aie_kernels/aie2/threshold.cc) | AIE API | Clipping | `uint8_t` |  
+| vision | [filter2d.cc](../../aie_kernels/aie2/filter2d.cc) | AIE API | Fixed point 2D image processing filter | `uint8_t` |

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.

programming_examples/basic/README.md

@@ -20,5 +20,4 @@ These programming examples provide a good starting point to illustrate how to bu
 * [Vector Reduce Max](./vector_reduce_max) - Single tile performs a reduction of a vector to return the `max` of the elements.
 * [Vector Reduce Min](./vector_reduce_min) - Single tile performs a reduction of a vector to return the `min` of the elements.
 * [Vector Exp](./vector_exp) - A simple element wise exponent function, using the look up table capabilities of the AI Engine.
-* [Matrix Multiplication](./matrix_multiplication) - This directory contains multiple designs spanning: single core and multi-core (whole array) matrix-matrix multiplication, and matrix-vector multiplication designs. It also contains sweep infrastructure for benchmarking.
-* [Hello World (printf log)](./log_hello_world) - Single tile performs a self-query and `printf` function where printed data is moved from local buffers to external memory to be read by the host processor.
+* [Matrix Multiplication](./matrix_multiplication) - This directory contains multiple designs spanning: single core and multi-core (whole array) matrix-matrix multiplication, and matrix-vector multiplication designs. It also contains sweep infrastructure for benchmarking.